{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>院系名称</th>\n",
       "      <th>专业代码</th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>政治__管综</th>\n",
       "      <th>外语</th>\n",
       "      <th>业务课_一</th>\n",
       "      <th>业务课_二</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>125200</td>\n",
       "      <td>(专业学位)公共管理</td>\n",
       "      <td>175</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>同济大学</td>\n",
       "      <td>测绘与地理信息学院</td>\n",
       "      <td>070500</td>\n",
       "      <td>地理学</td>\n",
       "      <td>288</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>建筑学院</td>\n",
       "      <td>081300</td>\n",
       "      <td>建筑学</td>\n",
       "      <td>310</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>建筑学院</td>\n",
       "      <td>095100</td>\n",
       "      <td>(专业学位)农业</td>\n",
       "      <td>253</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>南昌大学</td>\n",
       "      <td>艺术与设计学院</td>\n",
       "      <td>135100</td>\n",
       "      <td>(专业学位)艺术</td>\n",
       "      <td>347</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    学校名称       院系名称    专业代码        专业名称   总分 政治__管综  外语 业务课_一 业务课_二\n",
       "0  2020  中国人民大学     公共管理学院  125200  (专业学位)公共管理  175     88  44     -     -\n",
       "1  2020    同济大学  测绘与地理信息学院  070500         地理学  288     40  40    60    60\n",
       "2  2020    清华大学       建筑学院  081300         建筑学  310     50  50    90    90\n",
       "3  2020    清华大学       建筑学院  095100    (专业学位)农业  253     33  33    50    50\n",
       "4  2020    南昌大学    艺术与设计学院  135100    (专业学位)艺术  347     38  38    57    57"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('.././Data/考研信息.csv', encoding='utf-8')\n",
    "university = pd.read_csv('.././Data/大学信息.csv', encoding='utf-8')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>province</th>\n",
       "      <th>school_level</th>\n",
       "      <th>school_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北方民族大学</td>\n",
       "      <td>宁夏</td>\n",
       "      <td>双非</td>\n",
       "      <td>民族</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北方民族大学</td>\n",
       "      <td>宁夏</td>\n",
       "      <td>双非</td>\n",
       "      <td>民族</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>涉外办学</td>\n",
       "      <td>北京</td>\n",
       "      <td>双非</td>\n",
       "      <td>其他</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>宁波大学</td>\n",
       "      <td>浙江</td>\n",
       "      <td>双非</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>青岛大学</td>\n",
       "      <td>山东</td>\n",
       "      <td>双非</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   school province school_level school_types\n",
       "0  北方民族大学       宁夏           双非           民族\n",
       "1  北方民族大学       宁夏           双非           民族\n",
       "2    涉外办学       北京           双非           其他\n",
       "3    宁波大学       浙江           双非           综合\n",
       "4    青岛大学       山东           双非           综合"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "university.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "university.columns=['学校名称','省份','水平','学校类型']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44931 entries, 0 to 44930\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   年份      44931 non-null  int64 \n",
      " 1   学校名称    44930 non-null  object\n",
      " 2   院系名称    44915 non-null  object\n",
      " 3   专业代码    44918 non-null  object\n",
      " 4   专业名称    44931 non-null  object\n",
      " 5   总分      44931 non-null  object\n",
      " 6   政治__管综  44931 non-null  object\n",
      " 7   外语      44931 non-null  object\n",
      " 8   业务课_一   44931 non-null  object\n",
      " 9   业务课_二   44931 non-null  object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>院系名称</th>\n",
       "      <th>专业代码</th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>政治__管综</th>\n",
       "      <th>外语</th>\n",
       "      <th>业务课_一</th>\n",
       "      <th>业务课_二</th>\n",
       "      <th>省份</th>\n",
       "      <th>水平</th>\n",
       "      <th>学校类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>125200</td>\n",
       "      <td>(专业学位)公共管理</td>\n",
       "      <td>175</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>120401</td>\n",
       "      <td>行政管理</td>\n",
       "      <td>375</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>120400</td>\n",
       "      <td>公共管理</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>020206</td>\n",
       "      <td>国际贸易学</td>\n",
       "      <td>360</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>025400</td>\n",
       "      <td>(专业学位)国际商务</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    学校名称    院系名称    专业代码        专业名称   总分 政治__管综  外语 业务课_一 业务课_二  省份  \\\n",
       "0  2020  中国人民大学  公共管理学院  125200  (专业学位)公共管理  175     88  44     -     -  北京   \n",
       "1  2020  中国人民大学  公共管理学院  120401        行政管理  375     55  55    90    90  北京   \n",
       "2  2020  中国人民大学  公共管理学院  120400        公共管理    -      -   -     -     -  北京   \n",
       "3  2020  中国人民大学     商学院  020206       国际贸易学  360     55  55    90    90  北京   \n",
       "4  2020  中国人民大学     商学院  025400  (专业学位)国际商务    -      -   -     -     -  北京   \n",
       "\n",
       "    水平 学校类型  \n",
       "0  985   综合  \n",
       "1  985   综合  \n",
       "2  985   综合  \n",
       "3  985   综合  \n",
       "4  985   综合  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据大学名字，将大学信息和考研信息合并\n",
    "data = pd.merge(data, university, on='学校名称')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "年份        False\n",
       "学校名称      False\n",
       "院系名称      False\n",
       "专业代码       True\n",
       "专业名称      False\n",
       "总分        False\n",
       "政治__管综    False\n",
       "外语        False\n",
       "业务课_一     False\n",
       "业务课_二     False\n",
       "省份        False\n",
       "水平        False\n",
       "学校类型      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any(axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "年份        False\n",
       "学校名称      False\n",
       "院系名称      False\n",
       "专业代码      False\n",
       "专业名称      False\n",
       "总分        False\n",
       "政治__管综    False\n",
       "外语        False\n",
       "业务课_一     False\n",
       "业务课_二     False\n",
       "省份        False\n",
       "水平        False\n",
       "学校类型      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 连学校名和院系名称还有专业代码都是空的，这个几条数据不行删除！\n",
    "data.dropna(axis=0, how='any', inplace=True)\n",
    "data.isnull().any(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>院系名称</th>\n",
       "      <th>专业代码</th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>政治__管综</th>\n",
       "      <th>外语</th>\n",
       "      <th>业务课_一</th>\n",
       "      <th>业务课_二</th>\n",
       "      <th>省份</th>\n",
       "      <th>水平</th>\n",
       "      <th>学校类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>125200</td>\n",
       "      <td>(专业学位)公共管理</td>\n",
       "      <td>175</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>120401</td>\n",
       "      <td>行政管理</td>\n",
       "      <td>375</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>120400</td>\n",
       "      <td>公共管理</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>020206</td>\n",
       "      <td>国际贸易学</td>\n",
       "      <td>360</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>025400</td>\n",
       "      <td>(专业学位)国际商务</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    学校名称    院系名称    专业代码        专业名称   总分 政治__管综  外语 业务课_一 业务课_二  省份  \\\n",
       "0  2020  中国人民大学  公共管理学院  125200  (专业学位)公共管理  175     88  44     -     -  北京   \n",
       "1  2020  中国人民大学  公共管理学院  120401        行政管理  375     55  55    90    90  北京   \n",
       "2  2020  中国人民大学  公共管理学院  120400        公共管理    -      -   -     -     -  北京   \n",
       "3  2020  中国人民大学     商学院  020206       国际贸易学  360     55  55    90    90  北京   \n",
       "4  2020  中国人民大学     商学院  025400  (专业学位)国际商务    -      -   -     -     -  北京   \n",
       "\n",
       "    水平 学校类型  \n",
       "0  985   综合  \n",
       "1  985   综合  \n",
       "2  985   综合  \n",
       "3  985   综合  \n",
       "4  985   综合  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2017 = data[data['年份'] == 2017]\n",
    "data_2018 = data[data['年份'] == 2018]\n",
    "data_2019 = data[data['年份'] == 2019]\n",
    "data_2020 = data[data['年份'] == 2020]\n",
    "\n",
    "data_2020.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "学校名称\n",
       "山东大学          1422\n",
       "华南师范大学        1284\n",
       "青岛大学          1176\n",
       "燕山大学          1083\n",
       "福建农林大学         816\n",
       "              ... \n",
       "淮阴工学院            2\n",
       "新疆大学             1\n",
       "陕西科技大学           1\n",
       "中国民用航空飞行学院       1\n",
       "吉林化工学院           1\n",
       "Name: 专业名称, Length: 222, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2020年数据，按照学校分类统计专业数量\n",
    "major_num = data_2020.groupby('学校名称')['专业名称'].count().sort_values(ascending=False)\n",
    "major_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
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       "                \"\\u82cf\\u5dde\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u6d4e\\u5357\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u5730\\u8d28\\u5927\\u5b66\\uff08\\u5317\\u4eac\\uff09\",\n",
       "                \"\\u4e2d\\u5317\\u5927\\u5b66\",\n",
       "                \"\\u5b89\\u5fbd\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u4e0a\\u6d77\\u8d22\\u7ecf\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u5357\\u6c11\\u65cf\\u5927\\u5b66\",\n",
       "                \"\\u91cd\\u5e86\\u5de5\\u5546\\u5927\\u5b66\",\n",
       "                \"\\u4e0a\\u6d77\\u5e94\\u7528\\u6280\\u672f\\u5927\\u5b66\",\n",
       "                \"\\u6c88\\u9633\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u5357\\u660c\\u5927\\u5b66\",\n",
       "                \"\\u6e56\\u5317\\u5927\\u5b66\",\n",
       "                \"\\u5c71\\u4e1c\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u6c5f\\u82cf\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u77f3\\u5bb6\\u5e84\\u94c1\\u9053\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u4eac\\u822a\\u7a7a\\u822a\\u5929\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u539f\\u5de5\\u5b66\\u9662\",\n",
       "                \"\\u8fbd\\u5b81\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u4e0a\\u6d77\\u7535\\u529b\\u5b66\\u9662\",\n",
       "                \"\\u8fbd\\u5b81\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6dee\\u5317\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u5c71\\u897f\\u533b\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u6d77\\u5357\\u5927\\u5b66\",\n",
       "                \"\\u957f\\u6c99\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u8ba1\\u91cf\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u4eac\\u6797\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6842\\u6797\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u5357\\u4eac\\u4e2d\\u533b\\u836f\\u5927\\u5b66\",\n",
       "                \"\\u6c5f\\u82cf\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u6e56\\u5357\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u91cd\\u5e86\\u79d1\\u6280\\u5b66\\u9662\",\n",
       "                \"\\u4e0a\\u6d77\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u56db\\u5ddd\\u8f7b\\u5316\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u6cb3\\u5317\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u5357\\u6797\\u4e1a\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u5185\\u8499\\u53e4\\u519c\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u4e0a\\u6d77\\u5de5\\u7a0b\\u6280\\u672f\\u5927\\u5b66\",\n",
       "                \"\\u5927\\u8fde\\u5927\\u5b66\",\n",
       "                \"\\u5185\\u8499\\u53e4\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6e56\\u5357\\u519c\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6cb3\\u5317\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u79d1\\u5b66\\u6280\\u672f\\u5927\\u5b66\",\n",
       "                \"\\u4f73\\u6728\\u65af\\u5927\\u5b66\",\n",
       "                \"\\u9f50\\u9f50\\u54c8\\u5c14\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u4eac\\u534f\\u548c\\u533b\\u5b66\\u9662\",\n",
       "                \"\\u897f\\u5b89\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u4eac\\u4e2d\\u533b\\u836f\\u5927\\u5b66\",\n",
       "                \"\\u6b66\\u6c49\\u5de5\\u7a0b\\u5927\\u5b66\",\n",
       "                \"\\u4e1c\\u5317\\u5927\\u5b66\",\n",
       "                \"\\u4e91\\u5357\\u519c\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u4e1c\\u534e\\u5927\\u5b66\",\n",
       "                \"\\u5185\\u8499\\u53e4\\u5927\\u5b66\",\n",
       "                \"\\u7261\\u4e39\\u6c5f\\u5e08\\u8303\\u5b66\\u9662\",\n",
       "                \"\\u5927\\u8fde\\u6d77\\u4e8b\\u5927\\u5b66\",\n",
       "                \"\\u65b0\\u7586\\u8d22\\u7ecf\\u5927\\u5b66\",\n",
       "                \"\\u6cb3\\u5357\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u4e94\\u9091\\u5927\\u5b66\",\n",
       "                \"\\u6cb3\\u5317\\u519c\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u95fd\\u5357\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u5e7f\\u4e1c\\u8d22\\u7ecf\\u5927\\u5b66\",\n",
       "                \"\\u4e1c\\u5317\\u7535\\u529b\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u5357\\u533b\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u8d63\\u5357\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u5317\\u6c11\\u65cf\\u5927\\u5b66\",\n",
       "                \"\\u5c71\\u897f\\u519c\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6842\\u6797\\u7535\\u5b50\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u534e\\u5927\\u5b66\",\n",
       "                \"\\u5e7f\\u4e1c\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6c88\\u9633\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u666f\\u5fb7\\u9547\\u9676\\u74f7\\u5927\\u5b66\",\n",
       "                \"\\u957f\\u6625\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u8d63\\u5357\\u533b\\u5b66\\u9662\",\n",
       "                \"\\u5e7f\\u897f\\u533b\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u77f3\\u6cb9\\u5927\\u5b66\\uff08\\u5317\\u4eac\\uff09\",\n",
       "                \"\\u9752\\u5c9b\\u519c\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u91cd\\u5e86\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u9ed1\\u9f99\\u6c5f\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u65b9\\u6c11\\u65cf\\u5927\\u5b66\",\n",
       "                \"\\u53f3\\u6c5f\\u6c11\\u65cf\\u533b\\u5b66\\u9662\",\n",
       "                \"\\u6c88\\u9633\\u5efa\\u7b51\\u5927\\u5b66\",\n",
       "                \"\\u5c71\\u4e1c\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u5b89\\u5fbd\\u5de5\\u7a0b\\u5927\\u5b66\",\n",
       "                \"\\u5b9d\\u9e21\\u6587\\u7406\\u5b66\\u9662\",\n",
       "                \"\\u5357\\u4eac\\u90ae\\u7535\\u5927\\u5b66\",\n",
       "                \"\\u5e7f\\u4e1c\\u836f\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u6e56\\u5317\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u5b89\\u5de5\\u7a0b\\u5927\\u5b66\",\n",
       "                \"\\u6e56\\u5317\\u6c7d\\u8f66\\u5de5\\u4e1a\\u5b66\\u9662\",\n",
       "                \"\\u91cd\\u5e86\\u4ea4\\u901a\\u5927\\u5b66\",\n",
       "                \"\\u6b66\\u6c49\\u4f53\\u80b2\\u5b66\\u9662\",\n",
       "                \"\\u5317\\u4eac\\u4fe1\\u606f\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u5357\\u4eac\\u4fe1\\u606f\\u5de5\\u7a0b\\u5927\\u5b66\",\n",
       "                \"\\u5c71\\u4e1c\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u836f\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u9f50\\u9c81\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6df1\\u5733\\u5927\\u5b66\",\n",
       "                \"\\u5e7f\\u4e1c\\u6d77\\u6d0b\\u5927\\u5b66\",\n",
       "                \"\\u5ef6\\u5b89\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u4eac\\u7b2c\\u4e8c\\u5916\\u56fd\\u8bed\\u5b66\\u9662\",\n",
       "                \"\\u897f\\u5357\\u77f3\\u6cb9\\u5927\\u5b66\",\n",
       "                \"\\u8d35\\u5dde\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u6c11\\u822a\\u5927\\u5b66\",\n",
       "                \"\\u961c\\u9633\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u5317\\u4eac\\u77f3\\u6cb9\\u5316\\u5de5\\u5b66\\u9662\",\n",
       "                \"\\u6c88\\u9633\\u836f\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u4e0a\\u6d77\\u4e2d\\u533b\\u836f\\u5927\\u5b66\",\n",
       "                \"\\u6d59\\u6c5f\\u8d22\\u7ecf\\u5927\\u5b66\",\n",
       "                \"\\u6d59\\u6c5f\\u6d77\\u6d0b\\u5927\\u5b66\",\n",
       "                \"\\u5929\\u6d25\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u6d59\\u6c5f\\u4e07\\u91cc\\u5b66\\u9662\",\n",
       "                \"\\u5927\\u8fde\\u4ea4\\u901a\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u5b89\\u5de5\\u4e1a\\u5927\\u5b66\",\n",
       "                \"\\u5c71\\u4e1c\\u5de5\\u5546\\u5b66\\u9662\",\n",
       "                \"\\u5927\\u8fde\\u533b\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u5b89\\u5efa\\u7b51\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u798f\\u5efa\\u5de5\\u7a0b\\u5b66\\u9662\",\n",
       "                \"\\u957f\\u6625\\u5927\\u5b66\",\n",
       "                \"\\u5b89\\u5e86\\u5e08\\u8303\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u5211\\u4e8b\\u8b66\\u5bdf\\u5b66\\u9662\",\n",
       "                \"\\u6cb3\\u5317\\u5de5\\u7a0b\\u5927\\u5b66\",\n",
       "                \"\\u5e7f\\u897f\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u5357\\u660c\\u5de5\\u7a0b\\u5b66\\u9662\",\n",
       "                \"\\u5929\\u6d25\\u57ce\\u5efa\\u5927\\u5b66\",\n",
       "                \"\\u6606\\u660e\\u533b\\u79d1\\u5927\\u5b66\",\n",
       "                \"\\u6c5f\\u82cf\\u7406\\u5de5\\u5b66\\u9662\",\n",
       "                \"\\u6e56\\u5357\\u7406\\u5de5\\u5b66\\u9662\",\n",
       "                \"\\u897f\\u5b89\\u77f3\\u6cb9\\u5927\\u5b66\",\n",
       "                \"\\u897f\\u5b89\\u7406\\u5de5\\u5927\\u5b66\",\n",
       "                \"\\u6e56\\u5dde\\u5e08\\u8303\\u5b66\\u9662\",\n",
       "                \"\\u6dee\\u9634\\u5de5\\u5b66\\u9662\",\n",
       "                \"\\u65b0\\u7586\\u5927\\u5b66\",\n",
       "                \"\\u9655\\u897f\\u79d1\\u6280\\u5927\\u5b66\",\n",
       "                \"\\u4e2d\\u56fd\\u6c11\\u7528\\u822a\\u7a7a\\u98de\\u884c\\u5b66\\u9662\",\n",
       "                \"\\u5409\\u6797\\u5316\\u5de5\\u5b66\\u9662\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u4e13\\u4e1a\\u6570\\u91cf\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"axisLabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"color\": \"white\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"2020\\u5e74\\u5404\\u5b66\\u6821\\u8003\\u7814\\u4e13\\u4e1a\\u6570\\u91cf\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"horizontal\",\n",
       "        \"itemSize\": 15,\n",
       "        \"itemGap\": 10,\n",
       "        \"left\": \"80%\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"type\": \"png\",\n",
       "                \"backgroundColor\": \"auto\",\n",
       "                \"connectedBackgroundColor\": \"#fff\",\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u4fdd\\u5b58\\u4e3a\\u56fe\\u7247\",\n",
       "                \"pixelRatio\": 1\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u8fd8\\u539f\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                \"readOnly\": false,\n",
       "                \"lang\": [\n",
       "                    \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                    \"\\u5173\\u95ed\",\n",
       "                    \"\\u5237\\u65b0\"\n",
       "                ],\n",
       "                \"backgroundColor\": \"#fff\",\n",
       "                \"textareaColor\": \"#fff\",\n",
       "                \"textareaBorderColor\": \"#333\",\n",
       "                \"textColor\": \"#000\",\n",
       "                \"buttonColor\": \"#c23531\",\n",
       "                \"buttonTextColor\": \"#fff\"\n",
       "            },\n",
       "            \"dataZoom\": {\n",
       "                \"show\": true,\n",
       "                \"title\": {\n",
       "                    \"zoom\": \"\\u533a\\u57df\\u7f29\\u653e\",\n",
       "                    \"back\": \"\\u533a\\u57df\\u7f29\\u653e\\u8fd8\\u539f\"\n",
       "                },\n",
       "                \"icon\": {},\n",
       "                \"xAxisIndex\": false,\n",
       "                \"yAxisIndex\": false,\n",
       "                \"filterMode\": \"filter\"\n",
       "            },\n",
       "            \"magicType\": {\n",
       "                \"show\": true,\n",
       "                \"type\": [\n",
       "                    \"line\",\n",
       "                    \"bar\",\n",
       "                    \"stack\",\n",
       "                    \"tiled\"\n",
       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
       "                    \"stack\": \"\\u5207\\u6362\\u4e3a\\u5806\\u53e0\",\n",
       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
       "                \"icon\": {}\n",
       "            },\n",
       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
       "                    \"lineX\": \"\\u6a2a\\u5411\\u9009\\u62e9\",\n",
       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 1422,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"realtime\": true,\n",
       "            \"start\": 0,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"zoomLock\": false,\n",
       "            \"filterMode\": \"filter\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_23ed4eb63b6a4c07bed0745e03664571.setOption(option_23ed4eb63b6a4c07bed0745e03664571);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x20044059040>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Line,PictorialBar,Bar,Grid\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.globals import ThemeType\n",
    "from pyecharts.globals import SymbolType\n",
    "from pyecharts.commons.utils import JsCode\n",
    " \n",
    "# 绘制柱状图\n",
    "bar = (\n",
    "    Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))\n",
    "    .add_xaxis(major_num.index.tolist())\n",
    "    .add_yaxis('专业数量', major_num.values.tolist())\n",
    "    .set_global_opts(\n",
    "        # 标题\n",
    "        title_opts=opts.TitleOpts(title='2020年各学校考研专业数量'),\n",
    "        # x轴\n",
    "        # 设置x轴标签颜色为白色,倾斜度设置\n",
    "        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30,color='white')),\n",
    "        # y轴\n",
    "        yaxis_opts=opts.AxisOpts(name='专业数量',axislabel_opts=opts.LabelOpts(color='white')),\n",
    "        # 工具栏\n",
    "        datazoom_opts=[opts.DataZoomOpts(range_start=0, range_end=100)],\n",
    "        # 工具栏\n",
    "        toolbox_opts=opts.ToolboxOpts(is_show=True),\n",
    "        # 可视化配置\n",
    "        visualmap_opts=opts.VisualMapOpts(is_show=True, max_=major_num.max()),     \n",
    "    )\n",
    "    \n",
    "\n",
    ")\n",
    "bar.render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "省份\n",
       "山东     3449\n",
       "北京     2795\n",
       "黑龙江    2093\n",
       "广东     2069\n",
       "河北     1951\n",
       "福建     1723\n",
       "江西     1522\n",
       "浙江     1499\n",
       "上海     1440\n",
       "天津     1372\n",
       "湖北     1327\n",
       "辽宁     1311\n",
       "安徽     1287\n",
       "江苏     1181\n",
       "吉林      877\n",
       "广西      788\n",
       "山西      716\n",
       "重庆      579\n",
       "河南      572\n",
       "湖南      469\n",
       "四川      370\n",
       "内蒙古     201\n",
       "云南      192\n",
       "陕西      139\n",
       "海南       67\n",
       "未知       57\n",
       "东北       48\n",
       "新疆       46\n",
       "甘肃       36\n",
       "哈尔滨      36\n",
       "宁夏       28\n",
       "贵州       16\n",
       "Name: 专业名称, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据省份分类统计专业数量\n",
    "major_provience_num = data_2020.groupby('省份')['专业名称'].count().sort_values(ascending=False)\n",
    "major_provience_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'china':'https://assets.pyecharts.org/assets/maps/china'\n",
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       "</script>\n",
       "\n",
       "        <div id=\"d589d1b54def43c4899427011d363430\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
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       "                    document.getElementById('d589d1b54def43c4899427011d363430'), 'light', {renderer: 'canvas'});\n",
       "                var option_d589d1b54def43c4899427011d363430 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
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       "        {\n",
       "            \"type\": \"map\",\n",
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       "                {\n",
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       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\",\n",
       "                    \"value\": 370\n",
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       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\",\n",
       "                    \"value\": 201\n",
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       "                {\n",
       "                    \"name\": \"\\u4e91\\u5357\",\n",
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       "                    \"value\": 139\n",
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       "                {\n",
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       "                {\n",
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       "                    \"value\": 36\n",
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       "                {\n",
       "                    \"name\": \"\\u54c8\\u5c14\\u6ee8\",\n",
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       "                    \"name\": \"\\u8d35\\u5dde\",\n",
       "                    \"value\": 16\n",
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       "            ],\n",
       "            \"roam\": true,\n",
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       "            \"selectedMode\": false,\n",
       "            \"zoom\": 1,\n",
       "            \"mapValueCalculation\": \"sum\",\n",
       "            \"showLegendSymbol\": true,\n",
       "            \"emphasis\": {}\n",
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u4e13\\u4e1a\\u6570\\u91cf\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u4e13\\u4e1a\\u6570\\u91cf\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"2020\\u5e74\\u5404\\u7701\\u4efd\\u8003\\u7814\\u4e13\\u4e1a\\u6570\\u91cf\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
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       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"horizontal\",\n",
       "        \"itemSize\": 15,\n",
       "        \"itemGap\": 10,\n",
       "        \"left\": \"80%\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"type\": \"png\",\n",
       "                \"backgroundColor\": \"auto\",\n",
       "                \"connectedBackgroundColor\": \"#fff\",\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u4fdd\\u5b58\\u4e3a\\u56fe\\u7247\",\n",
       "                \"pixelRatio\": 1\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u8fd8\\u539f\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                \"readOnly\": false,\n",
       "                \"lang\": [\n",
       "                    \"\\u6570\\u636e\\u89c6\\u56fe\",\n",
       "                    \"\\u5173\\u95ed\",\n",
       "                    \"\\u5237\\u65b0\"\n",
       "                ],\n",
       "                \"backgroundColor\": \"#fff\",\n",
       "                \"textareaColor\": \"#fff\",\n",
       "                \"textareaBorderColor\": \"#333\",\n",
       "                \"textColor\": \"#000\",\n",
       "                \"buttonColor\": \"#c23531\",\n",
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       "            \"dataZoom\": {\n",
       "                \"show\": true,\n",
       "                \"title\": {\n",
       "                    \"zoom\": \"\\u533a\\u57df\\u7f29\\u653e\",\n",
       "                    \"back\": \"\\u533a\\u57df\\u7f29\\u653e\\u8fd8\\u539f\"\n",
       "                },\n",
       "                \"icon\": {},\n",
       "                \"xAxisIndex\": false,\n",
       "                \"yAxisIndex\": false,\n",
       "                \"filterMode\": \"filter\"\n",
       "            },\n",
       "            \"magicType\": {\n",
       "                \"show\": true,\n",
       "                \"type\": [\n",
       "                    \"line\",\n",
       "                    \"bar\",\n",
       "                    \"stack\",\n",
       "                    \"tiled\"\n",
       "                ],\n",
       "                \"title\": {\n",
       "                    \"line\": \"\\u5207\\u6362\\u4e3a\\u6298\\u7ebf\\u56fe\",\n",
       "                    \"bar\": \"\\u5207\\u6362\\u4e3a\\u67f1\\u72b6\\u56fe\",\n",
       "                    \"stack\": \"\\u5207\\u6362\\u4e3a\\u5806\\u53e0\",\n",
       "                    \"tiled\": \"\\u5207\\u6362\\u4e3a\\u5e73\\u94fa\"\n",
       "                },\n",
       "                \"icon\": {}\n",
       "            },\n",
       "            \"brush\": {\n",
       "                \"icon\": {},\n",
       "                \"title\": {\n",
       "                    \"rect\": \"\\u77e9\\u5f62\\u9009\\u62e9\",\n",
       "                    \"polygon\": \"\\u5708\\u9009\",\n",
       "                    \"lineX\": \"\\u6a2a\\u5411\\u9009\\u62e9\",\n",
       "                    \"lineY\": \"\\u7eb5\\u5411\\u9009\\u62e9\",\n",
       "                    \"keep\": \"\\u4fdd\\u6301\\u9009\\u62e9\",\n",
       "                    \"clear\": \"\\u6e05\\u9664\\u9009\\u62e9\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3449,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 0\n",
       "    }\n",
       "};\n",
       "                chart_d589d1b54def43c4899427011d363430.setOption(option_d589d1b54def43c4899427011d363430);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x2004405fb80>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制中国地图\n",
    "from pyecharts.charts import Map\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.globals import ThemeType\n",
    "from pyecharts.globals import SymbolType\n",
    "from pyecharts.commons.utils import JsCode\n",
    "map = (\n",
    "    Map(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))\n",
    "    .add('专业数量', [list(z) for z in zip(major_provience_num.index.tolist(), major_provience_num.values.tolist())], 'china')\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title='2020年各省份考研专业数量'),\n",
    "        visualmap_opts=opts.VisualMapOpts(is_show=True, max_=major_provience_num.max()),\n",
    "        toolbox_opts=opts.ToolboxOpts(is_show=True),\n",
    "    )\n",
    ")\n",
    "map.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>学校名称</th>\n",
       "      <th>院系名称</th>\n",
       "      <th>专业代码</th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>清华大学</td>\n",
       "      <td>计算机科学与技术系</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24456</th>\n",
       "      <td>北京航空航天大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31939</th>\n",
       "      <td>东北大学</td>\n",
       "      <td>计算机科学与工程学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5608</th>\n",
       "      <td>广东工业大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60051</th>\n",
       "      <td>大连海事大学</td>\n",
       "      <td>信息科学技术学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24830</th>\n",
       "      <td>华南师范大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24838</th>\n",
       "      <td>华南师范大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24837</th>\n",
       "      <td>华南师范大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24836</th>\n",
       "      <td>华南师范大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24835</th>\n",
       "      <td>华南师范大学</td>\n",
       "      <td>计算机学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           学校名称        院系名称    专业代码      专业名称   总分\n",
       "831        清华大学   计算机科学与技术系  081200  计算机科学与技术  359\n",
       "24456  北京航空航天大学       计算机学院  081200  计算机科学与技术  355\n",
       "31939      东北大学  计算机科学与工程学院  081200  计算机科学与技术  350\n",
       "5608     广东工业大学       计算机学院  081200  计算机科学与技术  340\n",
       "60051    大连海事大学    信息科学技术学院  081200  计算机科学与技术  334\n",
       "24830    华南师范大学       计算机学院  081200  计算机科学与技术  334\n",
       "24838    华南师范大学       计算机学院  081200  计算机科学与技术  334\n",
       "24837    华南师范大学       计算机学院  081200  计算机科学与技术  334\n",
       "24836    华南师范大学       计算机学院  081200  计算机科学与技术  334\n",
       "24835    华南师范大学       计算机学院  081200  计算机科学与技术  334"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算机专业top10\n",
    "data_2020_computer = data_2020[data_2020['专业名称'].str.contains('计算机')]\n",
    "data_2020_computer = data_2020_computer[['学校名称','院系名称','专业代码','专业名称','总分']]\n",
    "data_2020_computer.sort_values(by='总分', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>院系名称</th>\n",
       "      <th>专业代码</th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>政治__管综</th>\n",
       "      <th>外语</th>\n",
       "      <th>业务课_一</th>\n",
       "      <th>业务课_二</th>\n",
       "      <th>省份</th>\n",
       "      <th>水平</th>\n",
       "      <th>学校类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>125200</td>\n",
       "      <td>(专业学位)公共管理</td>\n",
       "      <td>175</td>\n",
       "      <td>88</td>\n",
       "      <td>44</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>120401</td>\n",
       "      <td>行政管理</td>\n",
       "      <td>375</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>公共管理学院</td>\n",
       "      <td>120400</td>\n",
       "      <td>公共管理</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>020206</td>\n",
       "      <td>国际贸易学</td>\n",
       "      <td>360</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>025400</td>\n",
       "      <td>(专业学位)国际商务</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份    学校名称    院系名称    专业代码        专业名称   总分 政治__管综  外语 业务课_一 业务课_二  省份  \\\n",
       "0  2020  中国人民大学  公共管理学院  125200  (专业学位)公共管理  175     88  44     -     -  北京   \n",
       "1  2020  中国人民大学  公共管理学院  120401        行政管理  375     55  55    90    90  北京   \n",
       "2  2020  中国人民大学  公共管理学院  120400        公共管理    -      -   -     -     -  北京   \n",
       "3  2020  中国人民大学     商学院  020206       国际贸易学  360     55  55    90    90  北京   \n",
       "4  2020  中国人民大学     商学院  025400  (专业学位)国际商务    -      -   -     -     -  北京   \n",
       "\n",
       "    水平 学校类型  \n",
       "0  985   综合  \n",
       "1  985   综合  \n",
       "2  985   综合  \n",
       "3  985   综合  \n",
       "4  985   综合  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>学校名称</th>\n",
       "      <th>院系名称</th>\n",
       "      <th>专业代码</th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>政治__管综</th>\n",
       "      <th>外语</th>\n",
       "      <th>业务课_一</th>\n",
       "      <th>业务课_二</th>\n",
       "      <th>省份</th>\n",
       "      <th>水平</th>\n",
       "      <th>学校类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>信息学院</td>\n",
       "      <td>081203</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>264</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>商学院</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>信息学院</td>\n",
       "      <td>081202</td>\n",
       "      <td>计算机软件与理论</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2020</td>\n",
       "      <td>中国人民大学</td>\n",
       "      <td>信息学院</td>\n",
       "      <td>081201</td>\n",
       "      <td>计算机系统结构</td>\n",
       "      <td>320</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>2020</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>计算机科学与技术系</td>\n",
       "      <td>081200</td>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>359</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       年份    学校名称       院系名称    专业代码      专业名称   总分 政治__管综  外语 业务课_一 业务课_二  \\\n",
       "70   2020  中国人民大学       信息学院  081203   计算机应用技术  264     37  37    56    56   \n",
       "80   2020  中国人民大学        商学院  081200  计算机科学与技术    -      -   -     -     -   \n",
       "121  2020  中国人民大学       信息学院  081202  计算机软件与理论  320     50  50    80    80   \n",
       "135  2020  中国人民大学       信息学院  081201   计算机系统结构  320     50  50    80    80   \n",
       "831  2020    清华大学  计算机科学与技术系  081200  计算机科学与技术  359     50  50    80    80   \n",
       "\n",
       "     省份   水平 学校类型  \n",
       "70   北京  985   综合  \n",
       "80   北京  985   综合  \n",
       "121  北京  985   综合  \n",
       "135  北京  985   综合  \n",
       "831  北京  985   综合  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2020_computer = data_2020[data_2020['专业名称'].str.contains('计算机')]\n",
    "data_2020_computer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>省份</th>\n",
       "      <th>水平</th>\n",
       "      <th>学校类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>264</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>计算机软件与理论</td>\n",
       "      <td>320</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>计算机系统结构</td>\n",
       "      <td>320</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>计算机科学与技术</td>\n",
       "      <td>359</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>计算机软件与理论</td>\n",
       "      <td>310</td>\n",
       "      <td>北京</td>\n",
       "      <td>985</td>\n",
       "      <td>综合</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          专业名称   总分  省份   水平 学校类型\n",
       "70     计算机应用技术  264  北京  985   综合\n",
       "121   计算机软件与理论  320  北京  985   综合\n",
       "135    计算机系统结构  320  北京  985   综合\n",
       "831   计算机科学与技术  359  北京  985   综合\n",
       "1624  计算机软件与理论  310  北京  985   综合"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mechine_learning_data = data_2020_computer[['专业名称','总分','省份','水平','学校类型']]\n",
    "mechine_learning_data=mechine_learning_data[~mechine_learning_data['总分'].isin(['-'])]\n",
    "mechine_learning_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['计算机应用技术' '计算机软件与理论' '计算机系统结构' '计算机科学与技术' '计算机技术与资源信息工程']\n",
      "['北京' '福建' '天津' '上海' '广东' '吉林' '重庆' '浙江' '宁夏' '江西' '辽宁' '河南' '湖南' '陕西'\n",
      " '未知' '甘肃' '云南' '江苏' '四川' '内蒙古' '广西' '湖北' '东北' '山东' '黑龙江' '安徽' '哈尔滨' '河北'\n",
      " '山西']\n",
      "['985' '双非' '211']\n",
      "['综合' '理工' '师范' '其他' '民族' '林业' '农林' '财经' '林业类' '工科']\n"
     ]
    }
   ],
   "source": [
    "# 专业名称中有多少个不同的值\n",
    "print(mechine_learning_data['专业名称'].unique())\n",
    "print(mechine_learning_data['省份'].unique())\n",
    "print(mechine_learning_data['水平'].unique())\n",
    "print(mechine_learning_data['学校类型'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>专业名称</th>\n",
       "      <th>总分</th>\n",
       "      <th>省份</th>\n",
       "      <th>水平</th>\n",
       "      <th>学校类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>264</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>4</td>\n",
       "      <td>320</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3</td>\n",
       "      <td>320</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>2</td>\n",
       "      <td>359</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>4</td>\n",
       "      <td>310</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      专业名称   总分  省份  水平  学校类型\n",
       "70       0  264   4   1     8\n",
       "121      4  320   4   1     8\n",
       "135      3  320   4   1     8\n",
       "831      2  359   4   1     8\n",
       "1624     4  310   4   1     8"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在机器学习之前需要对数据进行预处理\n",
    "# 1.将数据中的字符串转换为数字（用LabelEncoder进行编码）\n",
    "provience_type = mechine_learning_data['省份'].unique()\n",
    "major_type = mechine_learning_data['专业名称'].unique()\n",
    "level_type = mechine_learning_data['水平'].unique()\n",
    "school_type = mechine_learning_data['学校类型'].unique()\n",
    "# 编码\n",
    "from sklearn import preprocessing\n",
    "\n",
    "enc=preprocessing.LabelEncoder()   #获取一个LabelEncoder\n",
    "enc=enc.fit(provience_type)  #训练LabelEncoder\n",
    "df=enc.transform(mechine_learning_data['省份'])    \n",
    "mechine_learning_data['省份'] = df\n",
    "\n",
    "\n",
    "enc1=enc.fit(major_type)  #训练LabelEncoder\n",
    "df=enc1.transform(mechine_learning_data['专业名称'])    \n",
    "mechine_learning_data['专业名称'] = df\n",
    "\n",
    "\n",
    "enc=enc.fit(level_type)  #训练LabelEncoder\n",
    "df=enc.transform(mechine_learning_data['水平'])    \n",
    "mechine_learning_data['水平'] = df\n",
    "\n",
    "enc=enc.fit(school_type)  #训练LabelEncoder\n",
    "df=enc.transform(mechine_learning_data['学校类型'])    \n",
    "mechine_learning_data['学校类型'] = df\n",
    "\n",
    "mechine_learning_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 519 entries, 70 to 63578\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   专业名称    519 non-null    int32 \n",
      " 1   总分      519 non-null    object\n",
      " 2   省份      519 non-null    int32 \n",
      " 3   水平      519 non-null    int32 \n",
      " 4   学校类型    519 non-null    int32 \n",
      "dtypes: int32(4), object(1)\n",
      "memory usage: 16.2+ KB\n"
     ]
    }
   ],
   "source": [
    "mechine_learning_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "mechine_learning_data['总分'] = mechine_learning_data['总分'].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 316.3633941886193\n",
      "预测结果： [289.36687584]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 回归\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "lr = LinearRegression()\n",
    "X = mechine_learning_data[['专业名称','省份','水平','学校类型']]\n",
    "y = mechine_learning_data['总分']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "print('MSE:', mean_squared_error(y_test, y_pred))\n",
    "\n",
    "# 预测结果\n",
    "# 专业名称：计算机科学与技术 , 省份：北京 , 水平：本科 , 学校类型：综合\n",
    "message = [3,0,0,0]\n",
    "message = np.array(message).reshape(1,-1)\n",
    "\n",
    "print(\"预测结果：\",lr.predict(message))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 133.85576923076923\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn import tree\n",
    "\n",
    "# 决策树以及可视化\n",
    "X = mechine_learning_data[['专业名称','省份','水平','学校类型']]\n",
    "y = mechine_learning_data['总分']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "clf = DecisionTreeClassifier(criterion='entropy')\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print('MSE:', mean_squared_error(y_test, y_pred))\n",
    "# 可视化\n",
    "plt.figure(figsize=(20,20))\n",
    "tree.plot_tree(clf,fontsize=10)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 聚类\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = mechine_learning_data[['总分','省份','学校类型']]\n",
    "y = mechine_learning_data['水平']\n",
    "\n",
    "\n",
    "# 使用轮廓系数评估模型\n",
    "score = []\n",
    "for i in range(2,10):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(x)\n",
    "    score.append(silhouette_score(x,kmeans.labels_,metric='euclidean'))\n",
    "plt.plot(range(2,10),score,marker='o')\n",
    "plt.show()\n",
    "\n",
    "#使用肘部法则评估模型\n",
    "sse = []\n",
    "for i in range(1,10):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(x)\n",
    "    sse.append(kmeans.inertia_)\n",
    "plt.plot(range(1,10),sse,marker='o')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 2 2 2 0 0 2 2 1 2 2 2 2 2 2 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2\n",
      " 2 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 1 2 2 2 2\n",
      " 2 2 0 0 0 0 0 0 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 2 0 0 0 0 0 0 0 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      " 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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yoDfokDUaPLubG71uGY1Ww/TFU3E73Hz+r/UsOXLxgLiFEr8Z8Q0k29G7/ozOux7Q4tadhsd4yciOOZxlBJGBJEmkpKSQkpLC+PHjVde4q6uLrVu34nQ6SU9PVy0cg8Ew5DHjCaX/KYH9iGDC6bZEm3b2r+S1WCxUV1czffr0sIHccNKWwejs7OTqOXsK4IQt9HYX//XHnH7VCpUoFSSnm7D12jGaDAhZxuuWMSYbmL64CL1RT1KykW5LD7Z2O3MOmwn4yNFisahiU0lJHrKzJ5KdnT0s7Re96x/ovN8iyAa86D0vITTj8ep/NOh+ym8SKdpt/Tg9HsalhibFoSwiSZJIS0sjLS1NTYkrOsbNzc2qjrGSodLpdKNuwSjr/KFjnyeYoca0RmvBKIRUW1tLZ2cnixYtGtTEjpTALp58bZgDALLvv+fddBqnX7Ui5GbXP/X/uPv8/8Npd4EE84+eTVdbDwq3eTxeEJCeuydwaDAYGDNmDIVZb2D0PObzrmTYZTmOypbT1XEo2dnZA57qoW5irVyOIBUkLaAFoUXj3TAkwUBkN5Msy9zx9ed81uhLjU9IS+eeo44jNyhWES0ZhNIxVlLiO3fuRJIkVfkvVjrGg5HqviRHurexTxNMJLUt0VowHo+H9vZ2xo0bx+LFi4e8kCMN8no9odeQXZDJoact5bJ7Lxh0/9Kj5vBM/T/YWdFETqFEXs4matZtYu2/d1K3ORuEYPbR0wb2Jrl2keR5TP1T0kBh1vuk555Ln7OQrq4uKisr1ae6Mg4lFISUiUZ0I1BiUF6QckJuG7BfhDfU69u28klDPVlGExoJGnp7+eu3X/HXo44d1vHCITgl7na7+e6771Qd41i4loORoMvlCumm/RCxzxJMpLUt0TQvdnZ2UlVVRUpKCtOnTw94z+PxcNWSm2jc4ksp50/M4eGyv0Qc5NXptXjcAy2p/3f/z1h2yiLWvfU9L93zJrLHy6m//BFHnH3wgG31ej3TFuRicN6L5Olh5iIDk6e52balCGE4HGdS/wCyk8Q7CPYUz4EvRmzgHTIyfhdQ6OZfd+JwONDr9YwZM0btjHYbfonRcROS6AQEsjQGt/7cIT97pIRQ1WlGg4R2942ZqtdT1901YLtYuzOKYLjymwe7lkajUY3fRJoSD1UDo0CRKk1gHySYSKQs/REqKxTqmLW1tVgsFubOnUt9fb363oO//BfvPvnJgH3aGzr5zfI/cvmTPw1JMP43VX9/P5f+5yz+edaLAdsUTMnlkJVL+OyVr7nnokfUepb7Ln2U3i4rP7p4+cDPI1cj0YXQ+LJLpvRk5i2tx5V0MZs2bRq4DmkmwbeDBHg00wJe859OAFBeXo7RaKS5uZne3l41DZybdS8p+gqQjHi1R4AmRJZpkO9iMExMS0dGIISMJGmwez1MzMgY9vGGC8W1DNYxbmhoiFjHeCiCSU0d+nv7IWCfIhghBO3t7ej1+ohrHYZykZxOJ+Xl5WRmZrJ48WJVFwbg05e+CkkukkZCyILGLbsGBHlbd7az+p7/0t7QSWFxAct/djCWfjOHH34487fN566zHqRnVx8/ufUMDjvrIC6bdz3N21rV/XUGHR6XhxfvfC0kwfi+CPxMEglfECdMwNlwMLK7AA1t6q6CTDCcGfY7Ad/3lpeXR3JyMkL24rF/hHC8jKPbRUPXHLzaGWRn95OVZRjS3I+UEM6eOZtvdjWz1dKJhES60civlw605EY7IBusY6ykxIN1jLOzszEajcDgBKOk0RPYhwhGcYk6OjpIT0+P2MQczEXq7Oxky5YtzJgxQ60Y9Sekd5/6OPRBd9/EhiRdwPYOm5Nn/vAybqeHnMIsdlTvpOn2Zm5+7lpMySb6+/u5/j//jylTpuB0Orlo6rX0dvYGHNrj8oAEbnfoamJZMx0hpSHJ7QjJiCSseHQrB/0OHClvo3PchVauRJam4zZFJ+eglb/HqP0YkZpHSqpMTnY13a6ZtFtMNDc3I8ty2L4h39cVWUo2SafjH8eeQHl7G3aPh7l5eZz8ygv07A7gm7Q6vrng4rjU1USKUClxpWmzuroal8tFRkaGSjSh0N/fnyiy2429TjDBtS06nS7qrFCowrna2lq6uroGZIn8CSMjN3QjmthtQfzi3gsCrAbLri7sVgcZ+el0dprJyE3H1eehv8uOKdmkbvvsH1/hpbveGORDQ8lB00K/J2XiNlyN1vs+kujDoylF1h405PfgSfod4RsgBodG3oyQ0kHy3TSSxkR6cjMpGYuZMmXKgL4hZdibEiSFyFOyOo2GhWN8Q+iWP/e0Si4Adq+HQ599klePPymmFky0aXR/SNLA0b49PT20tLTQ3d3N+vXrA7rEdTpdTF0kSZJuBY4HkoFXhBB/jsmBRwl7lWBC1bZEqzgX7CIFu0ShunyV7a945CK+fP07tTwfQG/UU7JsGmfdcDILj5nHI797gm9WbyTJZORnd56Nw+HE2dpGVk42Oq0OV18vyWk+ApMkieduWcO618oGXXPJQcXcsvqasO8LTR4ezXkBrzXXtrFtXT2TJ08O//T0tJDk/CUSrYABl/5XeIdwlQCElIpGdiKUr0q48V3PPgT3DSkp36amJvr6+lTpBZvNFlX9TZd7YPOl1eOOuQUTS5dLo9GQlZWFx+MhOTmZCRMmBKTEt2zZwocffkhycjIOh2NEIvCSJM0GjhBCHCpJkg6okiTpASFEf0w+zChgrxDMYLUtkQRt/eF/IYZyiUJtrxBMamoq/9n1MLedcg9dbT3MWjadXz9xubrt3y97jA+e+Uz9+8/n/IOlZ83HZXFjNfcjBJxwyVEkp/tuxivm3OTrIQq7WPjD6utYetLCgM8/FG5Yfju1m+pBwCs3reW3L17FgmOCJgu42klyn4tEPxJ6wIHB/TfsmomgWzrgmP43saxdjlbehiTvAgRCysKrWxZ2Pf7D3oQQtLW10dLSosYs0tLSVO3e4aRrYx2DiZUWjD+UGEywjvGUKVOorq7mu+++47DDDuPyyy/nkkuGXQ3dBYjd5JIObAfClHDumxh1ggknZakg2t4i5Zjbtm2ju7t7yMK54POlpqZyz8e3hdz2g2c/G/Ba9Yfb+MsHf6C7vYecwizGTRvLueN+gdUy+O9uMOm54akrWHrSwqE/kB+euOlFajfWo9FpkDQSbpeHe372CM83Paxuo3PcgcH7ut9ebsAEONB73sUdgmD8ITS5uAxXoJHrAAlZUwxSZDEwSZIwGo2kpaUxbdo0VWjKYrEExG+ys7MHFLnlm0y02+0Bx8vSG/dpC0ZBuCBvTk4ORUVFTJs2jauuumpEAvNCiBZJkt4F/ggsBu4S+1kPwqgSzGBjWhVEW5nrcDiw2WxIkhTSJRoRQvyUTrubiTPHMXGmT6XuxxP/35Dkkpxu4uW2xwOP43Ris9mGVOfftqEWAK1OgywLNDoNjn4neD5B7/kCWc7DIF4PsacDEEjChtbzGULKQ9aU+FSmQkFKR9bOH/RzBEMIwab2NsqaGtG53UycMgWjTqcKTfnHbzo7O6mtrQ2I37x3zk848aXn2WX3fX85hiQ+Ov8CGhsbYzqTKV5SDeHW2N/fz7hxvutjJJaTJEmlwDQhxC8kSTIB30qStEQIER9hnzhgVAgmmtqWaAjGbDazdetWjEYjxcXFI1qjtceKKdUUMCEwKc2Ioy/wt5x1iC8463a7ue7QP9DXYR30uMdeeDjXPHJZwGudnZ1UV1djMplUklEqT4NditxxOWyl1ldDI0nIXpnf/GMHyc5fq9v4YtJ+bdiAQABGJNGHzv06SAKP9ii8+lOj+2IGwdt123l5SxXC68XpdtMgZG446BD0fr9vqPiNMpmgr6+Pv80uVT+70sW+v1gw4WJhdrs9VoV2k9hzj2oBAww7lr9XEHeCiXZMayRBXlmW2b59Oz09PSxevJgNGzYMe31lH2/mT+f9HVuvHb1Bxy/+9lOOv2g5VVVVXPPipdx7xqO4Hb7ftGBKPneu/R0Avz7iNnZUNA567HN+t5Kf/uEs9W9lnlJHRweLFi1Svw/FpaisrAxwKTIzM7nykYuo+HQzvWZfXC8t28tRpympbx+p+L7RQHPLy0QkaTxCM97XWyS86Lyf4dUdDlLWiLt9vbLMazVbyE1ORna7cUkatnVZ2GbpZFZueAXA4MkENpsNi8XCtm3bcDgcpKf7xr3EcvzuaLpIENNCu3eAH0uS9BW+H/h6IcTIhnqNMuJKMF6vl/b2djIzMyMiFxjagnE4HJSXl5OdnR3gEg3nqedyubjjnPtUK8Xt8PB/Vz2JzdDHQUcvZu7cuUytmozD4WDKlCkB++6sHJxc1nT/O+AJ5/F4qKysxGg0snjxYvX8/mnQyZMnqy6F0jdjMBi445Pf8OUL66nf2sh5VycjaSp3HzU0SQiS0GhyEGh2Ny6ym2QkJOFSs0UjsRK8QuAVAp0k4RS+4kSNJOGOIgPoX3OizM7u7e2lrq6OhoYGWlpawsZvolprnPR4w62nv78/JgQjhHADPx7xgfYi4kIw/oHcLVu2cMghh0S872BZpI6ODmpqaigpKVGj9so+w8kUNFa1DHCBAN6662POuGAVEF5wSqPRIOPTawmWtLzxv1cHkEt/fz/l5eVMmjSJwkJfC0A4Ky3YpbDb7VgsFmafWMzYg7JJyviCAc1HgEwuoEVI2aAZjyQ6EJIBSe5ASBlIohtZk+97PwYwaLUsHjOWdbtaMCFhdbnITU+nKHP4Q9iUruiMjAz1v1/W1fKXzz+h3+nkoKxsVk0viVpGM55ZpFBIVPLuQcwJJtglihahLBjFJert7WXx4sUDfF9ln0guoh5zH3XrGzDZ0xhTFHq+UZ9lT5lBuFaEH120nLce/WAPuUhw5SMXM25hHnPn7kkhK2NX586dOyyFeZPJxLhx4xiX+R0VeEhLS8MjG9Bp9mTavEIPuklAKkiG3ZWCAo/+LLTeCiS5CVlbglt/GkhB40K8VRiddyAJC7I0A5fxT6DdIwnR7XDw0pbN1Pf0MCE9nbNLZpOzO1Zy6bwFZBiT+L6pkQKjkcsPOpS0GHQRK9ZoTU8Xd278Dq8so5E0vNreiiE1lSP7++nr61N7hvzjN6EQLxcpocc7NGJKMMFjWmNBMIpLlJOTo8YtghGpZIO52cK/f/s8zfUtbH2/nozcNLQ6zQCphYIJe2powglOXf73C8kek8l7T32CzqDjp7efRemxs9ixYwfgu0mUONGSJUuG377f/y9MPIIEHDQFZAkkxiBw+fqOZCed1sNp65rC5PwP0Om06PQ60C1E1ixC1oZPUUtyGybH/0PCiUCDTnyL5LwUZ/JLAHhkmYc3rqfF2keG0UiV2cyDG9Zx08GHYdBqMep0/GT2XI7LzcfhcDAmRk9thRD+u2ULLq+X7CQfedg8bj5q28XlBx+KEIL+/n66urqoqalRVe2U+hv/uUvxyiIluqmHRswIRnGLhkssCvwJRnGJZs6cOWjQL9Lq309f/gqHzUXmmHRyc3NorW9j7oklbFpbpYYzdAYdt6zZk6EZjLzO+e0qzvntKvVvq9WqatgoMpzhSDFSKOSirgfw0ImkWQDCimxYRmrBZaSO1eHsX0aftZrudkF790TS07eFvOHA93sZ+R/gROCrGxIItGIneHtAm4HFYafF2kd+su9myUvW0W6z0W7rZ3xaesCxYt07FPp4kirAJUmSOujNP35jsVhobGxECKF2RHs8nlGPwSQsGB9iRjBK+jnU0z6aC1CJeWzdupW+vr6Inv6RVv/aeuwYjDrsHje9vb043U6WLl7IZXdeyNpH3sOQZOCcG08lNXPPkzjaqQIul4vvvvuOqVOnhh0xi+1J9M770AMCPQ7NxxDGxB/wrQnQSG4cpkcGbGtMmYMxZQ6ZBTAx6IYDyMrKIicnx+/iD3fT+V43aLQI4csYaTUaZCGQhcAQotkxHmnlk6dO4/36Onqczt0BZC8/nT035D7+qnbgs6a7urowm810dHSoD65YjUEZjGAcDsc+N5t7byGmLlIod0L5YSMtnFIK5/R6fcRP/0hdpFmHzmDLuu24JAeyR2AyJDPnsJlMmF7I5fdfGPYzRdobZTab6enpYdmyZeGDfPYP0Tnv23N83Jjkw7ATOtUeIp4b8pVgBN9wbrebrq4uVWTJ5XLR1HEQ03KfR8IGaBDIeKXZagwmMymJoydN5v2ddUhICARHTJhEnimwUzheFszsvDz+tvxYnqoow+nxcPyUIk6fMTOiY+h0OvLy8sjLyyMpKUltpPXXfFGsu+GQgRAirFU02Hs/NMS9Dkbpjo6EYJSAqNFopKioKOJzRFqcV7x0EkWHj2fr57WkpqRyzM8PY9bBe5Tt/nbhw3z60lfq36uuPoGzbzo1orqcmpoarFYrGRkZg2YQNI4/DHhNArDbQ1oxLiZjZCewp8vbwUODricU9Ho9+fn55OfnI4Rg48aNeEQ63++4iSm5T2Iy9OHRzEKYbgu4KE6fXsK07Bxad7tK8/ILBpBJrKvX/WMmCwrGsKBg6LG9g8Hr9aoTOpX6m/7+fiwWixq/UTqiQ7mT0WA/q+SPO+JOMFqtFo/HM6h+hizLbNu2DavVqk5TjAZDWTBCCJqammhqauLcX53O9pO2M3Xq1AAi+OSVLwPIBeD1B/7HYecsQWjDXzRK93Z2djZz5syhsrIy7LY+hCNCCzBu4NYpa7D1/xkj/0X2arFoHyQtdcEQ5xgcijtbWFiIwTAZWT4WS08Plk4LXV1Vasew4k7Ny8tnXl5+2OONXgxmeAg1BkWJ3yhTCZSOaP/4jVJ/MxxrJCH67UPMXaRgDGVd2O12ysvLycvLY+HChQHHcNicuB1uUrNSBv3BBgvyer1eqqqqAFi6dClarTaAkLxeLzcecwdbvt0ecv83/v4eP7r28JDvdXd3s3nzZrV7W9EQHgyy8TI0fi4S7I4vmwaSi4qUm3ByE9XV1YwfP37Q4w8HCqFkZflqWBTNWqWcX3EnBhtyti+X9g91vODPr8RvOjo62L59O3q9XnWnhorfJCYKBGJULJhwBKO4RLNmzVJ/XAVvPPQ/3n3yEyRgyryJ/L/7f0ZqZujUnxLkXb++gr+f9QinXbeCM65aoRa4TZgwIeDG9CeYf//2P2z9rhZJUoXsAjBuxpiQpNHU1ERjYyMLFixQ1csiCggnX4THuxmt5121e8iueXHwfUYZ/pq1/u7Eli1bcLvdZGRkqDecVqvd7yyYoeAfvwFfXDBYs9flcoXUexmpBsyBhlGJwQQPm1diFv39/QOyRE3bdvG/v39G/ffNFEzKQ6PVUFtWz4t3vc6lfzk/5Dk0Gg0/L7pB/fvJ37zAk795gZs+/CVz5swZUODmTzBbvt2OhITepMdpC5KJkODM605h8+bNAWuvqqpClmXVIlI3jzQgnHYPbvmvavNnJOg199HZ2EVOVl5MUqCR3sTB7oQyc8hisbBjxw50Op+saHp6esyIYbQtmKGQlJQU0D/V29tLb2+vOlVSIdzMzMyY18BIknQlcAjwRyHElpgdeJQw6i6SzWajoqKC/Px8ZsyYEbDPmw+/y+M3PIeQfVZAn8XKjCXFpKSbqC3bGfa8tRubQ75+zymP82rnkwGvCSFoqmqlaVMb0+ZOpWBSLts21CFkgTHZoJJM3qQcHt54d6Amr8NBWVkZY8aMYeLEiQM+b7iivJHiy9e+5dMXv8FqtfJ1/iZ+cssZA+cjjRDft+7i08YGjFotJ00tZnJGZsjtgmcOOZ1Otm3bhsViob29ndTUVPX9weJug2FvWzCDQZIkkpOTMZlMlJaWBsRvNmzYwK233oper+eTTz7h4IMPHvZ3sPtcvwY2CCGij+rvIxhVF6mtrY3t27eHdIkA/n3j8whZqO6Ky+6mrb6D5DQT46aNDXuOx69+JuTrLlugYpwQgjce/B/vP/cJWr0Ove4Tjr/wCCo+q6a3sw+Q0CfpueWla1l0/DzAl94VQqhjZsOtHaInmEhupOZtu/jkxa/JGZeNoV+H1+bl9X+8w+UPXBjxeYbCty3NPPj9dxi1WryyYEPbLm4/7MiAYrpwMBqNpKamkp+fT15eHlarFYvFQlVVFR6Ph8zMTHJycqJqVtzXLJhg+NfA+MdvioqKSEtL4+6772b16tV8/PHH3H777cM6hyRJ+cAqYOluXd5/CSGej9VnGC2MmotUXV2N3W4PWzjncrnwemQkDSgyBAiwdlnJHZfNebecHvYcJ162nFfufGvQdfznT6+y+p43cTk8pOelULyoCJ1GxwfPfM4D6+7i69fWYeu1cdgZyyj0sw4kScJms7Ft27aI1PJSjM1obPcBLoT+WIR+cchtOzs72bVrV9hKWwVdrd1IkoTBoEPq942O7WrpxuPxxEyU6e267STr9GofUbutny+aGjl35uyI9vcfjqfMjJ40aZI67E0Rm1KCpdnZ2aSkDB64j7UFE8tmx8GK7HQ6HdOnT+ehh0ZsdKwAdgAXA0bgK0mS/ieE6BzpgUcTcXeRPB4P9fX1TJo0iZKSkrAXjsFgUKcjSn4Pm6PPO5wLbj9L1b0NhhCCBafMDkkwa+3PAfDcH1/hRT+V/96Ofmq+q2XeYb4bSJJlTv5/xw3Y3+v1snnzZmRZZsmSJUM/Bb11TBvzHJInHdAgecqQxbUIw55+ICEE9fX1tLW1MXHiRHp6emhoaECj0ZCdnU1OTk5ApiJrTCbCK3C5fHGsno5essdmjZhc/K2nUHoy0dze4ay24GFvDocDi8XCzp071XL6cEJbsUSs5RpGSQumAPhQCOEBPJIkbQHGAvsVwcS13LCtrY36+nqys7OZPHnykE+lX/3z52i0vqFnCChZNo3/d//PwpKL2+1m48aNeL1e7tl4C8bkPRfpjc9fxSv3vIml1cKa+waSj7XTpurqZuQPnC5os9lYt24dOTk5JCUlRXSBalyfISGDJgs0GSAlIbnfVt/3er1UVFTQ39/P4sWLyc7OpqioiMWLFzNnzhySkpJoaGhg3bp1VFVV0draSt6kHI46/1AsLd10NvWg1elY+asThlxLNFgxdRp2j4duh4NOuw2DVsuh4ydEvH+kMZOkpCQKCwuZM2cOS5cuZfz48TgcDiorK1m/fr06aiaehXuxwChJNWwFlgNIkpQGTANqY3Hg0URcXCSll8hut1NSUkJX18D5w6Fw9HmHsfiE+bzz3HsUzZ3MkqPCC2T39PRQWVlJcXExBoOBXbt2sabzCVwuF+eP/yV/Of9BAJ7+/ctImtAX//gZhZz/+zPQagMvPqXJcs6cOWRkZNDQ0BDZB5c0QUOiBQqHOxwONm3aRGFhIRMnThxwEwWnhv1V7gzjJU79zdFY2ruZUTqNgrGxDfAuGVvIr5cs47MmX5D3xKLiiOIv/ojWpQkltNXV1UV7ezs2m42ysjLVuol0ymc4xDpoPJhFFMMs0hvAKZIkfYlPJvMGQJYkSQu8ClwNPAqs2G3lDAu7Jxa8JYT4URT7PC+ECJ3SDULMXSSbzUZ5eTljxoyhpKSE3t7eqES807NTWXLyfLW2xOuVefDyf7Hu7Y0kpRi47onLyZiYQlNTE/PnzyclJYWenh4103Pbyr9h73MEHFPJSgWcJz+VXz1yaeB2QlBXV4fFYhmWxIKsPwpZfg1kM6AF0Q+aAvq6vqS8SjBz5qyQXeEOm4Omra3o9FrGl4xFp9MF3HxKH1G/u5/aHbV0dHao7tRIshT+mF8whvnDLMmPxQ3sX3vS09PD9OnT1UFvNptNlWLIzs4eVin/aMV0YqhmJ+OLv6iQJOkM4FjgN8DNwP+FIhdJkh4UQlwlSdIs4FghxD9CnUOSpBRgFpAhSdI9wM3BguKSJP0Y+AeQBMwH8vDpA0eEmBKM3W5n48aN6pMf9rQKRAONRsOu2jYev/I/fP9BOW6nb//eTvjtsXdy+b9/wgnnHKf+yP6VvDsqQlsb0xcXUbO+DoC0vFT+8O41Ae+73W4qKipISUlh0aJFwzOptROo2XUBi3LakTyVSHILXttqTJ4XWTZ3Fdr0wwbs0tXWzZM3vYhlVzdCwJQ5E7jgjrMwJO0hN6WPqLu7m9zcXAwGQ0CmRilrz8zMjHjdsXyqx6N6VRXaGjcuYBRKU1MTQgiVbNLT00e9sXAoF8lfbTHG2ACUAtcA7fgGsR0DfCWEsANIkpQDZEqSZAB68cVvThdCvBrieBcBrUA+PlN7Ar7ZS/74FngeuAA4Gt98pl2RLjimBGMymVi2bFnAlx/tGBIA2SPz7C1rkF1CJRd/rP3bR6w4b08cQqPR4HS4+M+fXg0pgQnwt09uU12htrY2+vr61PesVivl5eUUFRUxZszIGuvs7gLkpB+h7TkDh8uAEBqSjKlI4h083pWgnRyw/TuPf0R3ew8Fk/MQsqCuvJ6v39zAkWcPHAqvZGqCC98UWYLt27eTlJSkWjejJRkQa4IJPpZGowkYhaJYdK2trdTU1KifWVG2i3ep/lBaMPEQm9qdqr4bnxD4T4EM4L/4gsGrgM8kSbocH2mk4tPyvRR4jhANcJIkTcYXNP4VPhesGd+gN/9t/oFvwFYn8C7wzO7zXR3pumMegwn+4qOdNQ3Q227F3usgtzD0k8BpCyQRjUbDt2s2UvVBLeNLxrKjPFCQO2dcFhq/OIx/8Vxrayt1dXXMnTs3ZiJBbmcnHqcTSZOK0WjwjSISOiTRPUCm29xkIWV3EFvSSBhMBsxNkcWswPd9+2v4Kir9SpewIpqtlPXHA6PdfxPcGa7oFiuTJdPT08nJySEzM3NEndHh4PV6wx43lnOpg6BkOo4EngIeAs4UQmz22+ZJfB2zJwKNQBtQjG9IVjBad79/B7AQ6MNHWmqWSgjxK0mSbgD+CfwLyMVHXBE3xI1KJW+0LlJqVgqyLON1e5A00oAYynEXLh9wjp0bG0lOM2E0GcgsyKDX3IdOryV3fA4X3H5WwNqU3qWtW7eq7QojvhD7/4jW9R5Lp8K26nMpmZiLXtcL6EE4EOgQmskDdptQUsj6/5VhSjMhyzJjx+/ixNOrMNrfwa0/B1kX6FYNlWFJTk4mOTmZ8ePHI8uyOrReKetX0saxzNTE2t2KBkplrf9n7u3tpbOzk/r6eiRJwul00tPTQ1paWkzcqaH0eONBMEKIG3fHVDKBF/G5NY9LkpQmhFBUuI7AR0BP4rM0eoDZQogbQhxyAqDEeZQPs0KSpG4hxM8AJElKBurwWUAbgLeB83afPyLE3IIJrmaNRhFOQWZeBgeftZDPX1hHQVEubXVmH8lIsPycQ/nx704L2F6j0ZCcaaK7wUpSipGJM8fRsr2VOYeVcMovf8SMJVMDtvd6vbS1tTFhwgQWLFgw4ptD03s5Gq9P7T9JD3MnP4rHdBvC/TaStx6hycWb/HvQZA7Y94RLj6artYe68gaKZrbzy1s/Q6MVIIPW+S1OcRte/UlA9IFKpbZGCSwrdSh1dXVYrVZqamrIyckhOzt7RHU1+xJZBQttuVwu1q9fT0tLC729vSQnJw8Y9BYtBgvyxnmiwBJ82aUHgUeFECcrb0i+yY9TgOvwBW6/AQ4C7pMk6TLgSSGEa/e2f8RXxLcN2AzcC5wB3COEUPtuhBA2YI0kSVcB1wI7gTol3hMJRn02dSTo7+9nwkEFXHXEJbj7POSOz2HK3Ilht9doNBx09nw+/r919HT0IoRg6vzJ/PKBCwfU0PT09LBlyxZSUlKYOnVqmCNGB4Vc/KG1P4w360MQHpDCf82m1CQu/tPZSNYbMGk/B0nG90DRAl70rodUghkplDqUwsJCvvvuO8aMGaN2CYcr9IsU+2rAWKvVYjQamTlzZsCgt2AXMjMzM2KSHY2ZSKEghHhakqQi4FSCAq27b/pHJUlaDqQAJcBNQgibJEkrgE2SJP1GCLFWCPEHSZLOB34GnC+EEJIkteLLTgXEVyRJegjIAsrxxWPe2V2Xc1gkRLNPEYwQgh07dtDa2kpubi6lpaH1V4MhSRJZhRnc8tI1bNtQh1avY9Yh00lKDkzhNjc309DQQElJCa2trVGvLVylsk4hl4DprbvjRJKOz1d/zc6qJg5ZuYSppZMHHCPJcT4a3TbfvgJAxuOW0Rk0SOzp8I5lM6UkSaqKW1FRkaoBo0gSRFNlG/zd1HZ38cj36zHbbczPH8Mv5i8kOUIXNJ59SFKIQW+KC7lz584Ai2+wmeF7i2B2owFoAUKqsgkhPgE+CXrtr8Bfg157Hl92SPn7OXwB4eDjXTmSxcbdRYoU/mniuXPnUldXF/UxMvMzWHLiQLU3WZZVLZMlS5bgdDoj1tmF8IPd+vv7KSsr49AZaWg1fQHveQ0rALh09rW01nUA8NJdbwwYJ4vXi4ZtAywgjdaXTfMaj4h4nSNBqEK/zs5OKioq1LRwTk4O6enpISUzldc67XZu/vQjnF4vSTodHzbspM/l4veHhhbtCsZodlIHu5CRCm0N1SoQz4kCu+te3hhyw30Eo2LBKKQT7sLxr8otKCjAbrdHnXkKB0VioaCggEmTJiFJEm63O2qCCSBNjwV3183Ijnbmz/wZIu09RN+JSHQDIGuPRHK9gMbxH574FG6/ZBzffZiP7JV5+e43AwkmHAR88kYGy35yS8DLdT093LDuK3odTo6fUsQv5oevdh708IP8Hv5Vtv5p4ZaWFtW99C/08z9WdWcHTq+X9N0FgFkaDd+1tuCWZfQRWCZ7s5M6nNBWdXW12hmenZ09KMG4XK6YFT8eCBgVglGyNsE+rr9WrlKVC8OrnQmFrq4uqqqqBsxVinQKgQJ/ISnh7kDXdww6rcCUCnAzHtdFeLM+B+Dbb7/lkOmXojSFSxLc/mQzvzlHT+XXWcheGUtHL1m5u59yWi2CLKTAEgR+f8EUNnySzqsXaHC5XHyxeh3rqrfxcpEbGZ/Bs32jhca+Xu44fPlwv6KIEJwWVm48pdDP4/HQ29tLUlISRq0OGaGSjkcItBoJbYRWyb6iBROq3khxp5QHohIg949ZxWqigCRJeuA/wP1CiK+G2n5fxagQjCLZ4E8wSqeyJEkDlOFGSjBCCBoaGmhtbQ0psRAtwSjbe71evJ0XoTeIgJ4jnfNJPMnXASBJ9SE7ke9+YScnTchAkiSy89IDLCJ70mt0Vx9PwQQXHrfEY7eNZcMnvl4gr9fL479+ntqNO9l8WAZekYKEhFYjIQvB27Xb4k4w/gi+8TweD5s2baKzs5OGhgZ0BgPjjSbqbf1qYeAl8+ajiWKOdKwbE2NxPP/O8O7ubmbNmhUgo5mamsqmTZtiUncj+djq//ClkuPXZj4KiEsMJhjBhBFOK1dBtASgQAiBLMts3rwZjUYTVmJhOARjt9vZtGkTy6ZZQ2sZON8E46kkSeY9rwUEfX1///jmVepau7u7SU9PR6tNI3PO16zICGg94dWeJ9i2oY668nrGFOWzNVWo+4rdi4j+W/JbTgwsBZ1Oh16vp7i4GKPRiM1m49dZWbxfV0un3cbs/HwOy86NeHZ4PCyYeBQYBsto9vX18fLLL9PQ0MCyZcs45ZRTuOWWW4Y+UGjcCfyPkf28+wRGxYLxJxilcjaUVq6C4VxgWq2W/v5+KioqGD9+PBMm7JYbkM0gekEzFiRf3UO0BON2u6msrGTOnDlIuqXgeWfANj07H+Ke66tweZ0sfI49JKSQjNDw3NZjyZxwBm63m7KyMjQajapan5OTw3O7HlKbPBW4nG4kfGNGpjTYaBrre0IqFtDc3Lxovqa4Izk5maJJk7hst+BUT08PnZ2d1NXVqZ9zsA7pfV3NLhSUmNUf/vAHPv74Yz766CO2bt063GP9BOgUQrwqSdLxsV3p6GPUXCS3282WLVuw2WyxqZwNgmKqz5kzRy2y0jieR+N8BtAiMOFN/Qtoi6MiMMUELi0tJSsrC8FfoSuIYAR0tdmw9Tno7uzmg9cKOfa0lgCS0RiKyDW8RJ/1MPrb72HRlGY0unG4jNdid42ls7NTLXVXZCYzMzOZNHs86blpmBs7STXATJuDHYfnoUnWU5qXz9+P/RH9bhfbLBaMWh0zcnIidkdihXBWR7B+r8PhUMnGbrerJf1ZWXsEtPaVGMxwYLfbSU5OJiUlhYULhxd8B84G0naTyxxgiSRJ1wghPo/ZQkcRo+IiCSGorq6msLBwgND3SKHUzjgcDpYsWbLHKvJUo3E8A1ISSFokYUXbfxve9AGpfgAsrd3sKKtn6qLJZOZmIMsy1dXVeL1ecnNzAwhRSKVIoixg/7dfWkJymgmPcPOfB+aQnHs+hyx/xXf+3fB6vHh6f0d+lgVJSkHIdRgdNyJMjwV0Disyk3V1dRgMBlbecCxfvLCelrpWlo8dx7/OPZPUDF9AvLmvj+s+eg+r24UsoDS/gDsOXx5RxiZWiEZwyv9z+pf0KyljvV6/3xJMLLRghBCnKv8vSdJL+IK834x0bXsLcbdgOjs7aW1tZcKECVGNg40EHo+HiooKTCYT2dnZgSNE5CZ8aRzltWQkeVfIytpnbnuZV+9/e3cGQOLcm1YxZXkh+fn5TJo0iaqqqoCgrDfzOUT3b9DKXyJJyfz9t9No2J5JUgpISCDA6R6LQIskbIAJj7sHh0tPZmoXkiZ9dwwlBQkHGrkGWeuT1QyuzVAqTw+7eBF9fX2kpaXh8jqRZRMajYYH1n9Lt8NBqsGAEILv23bx3o5aVkydFtPvejAMx+oIVdKvXCtWqxWPx6O6UyOxdmNNMIPVeMWhTSAVsMbygKONuBGMYlmYzWYmTpw47AHj4S5cRWJhypQpjB07lvLy8oC4itCMwycc7t1NMnaEpmAAudRVNPDq/W+h1WrRGXS4HG7+c+er/PWEPzB58mRg4LyjzV9u5YPnpoGYRnpOGuPnplP5zUf099iw9dvJH59L6dEH400Zh9b2RzyuVpzuPAw5dyI5rsIXu9P6FO8kmbf/9RWP3fRPAA5esYgbnrlCPZd/I19dXR1CCFWawWQyscPSiUG7p1JVCEFTX2/U3/VIEAu3xmAwMHbsWPR6PT09PeTl5dHZ2UlTUxOAOso2VKHfYBhNucxYz0QSQqyI2cH2EuLiIvlX5S5evJiWlpao086KiFSoH7OtrY3a2toAiYUBgVvdLOSk89E4nkegBZLxptw24Fhb121HyKAz6ZCFjEYvITvAXGdhhm9yScCxzc0W3n/6E7LGZKI36ulu78HQreeyey+g/PMq+h1WVl5+EunZqbjd01hfcz1ZWWkUFU33EZW4AI3jWXwSHBJbNmbw+I1lIHw3zddvbOCmE/7Mn/9304C1ajQakpOT1XoUm81GUUsj6zraSJI0aLRaQDAtc6BqXjwR625qZZDbYIV+inUzVFFbqPqrkWBvaMHsz4g5wVitVjZs2MDUqVMpKPBpx2q1Wlwu1xB7BkIpzvP/MYUQbNu2jb6+vgGB4lC1M3LSBciGE0B0g2Y8SAPFw6eWTgIJ3C43Gq0GZJ8ui39zpX/7Q09HL5KkQW/0nTszP4O2+g7Ovek0Fh43j8rKStKyU9Q2gmARK6/xfLzSVPBsRUh5XH/qfxAi8Obc8vX2AU9ep8fDey1N9Mkyh3s9LB1bSEpKCjcvP4abPvuY7V0WXF4vh+fkkdLWTqW1X63biKdiP8S2mzqUxRGq0K+zszNA0U+ZvRS872jW1YxCH9J+h5gTjNFopLS0NIDJh1M4F7yPy+WivLycjIwMFi5cGFL1LGTqWZOPTzpjIIQQTCmdyNxjp1P+/lYkj2//ky8/lsKpe0jB/9hpWam+ojuPF61OS5/FSlZ+hqqWJ0kS3d3dNDU1MXfu3IBUvFKnI2sWIxmXIEkSXs8LIdfm9XrVz+8Rgp+/+xbbuyy4ZZk1O2u5atFSfjxzNulGI9ctOYgr338Hi9vF190Wzlq4mAlJSXR2dlJZWYksy4P2Eo0Uoym/6V/oN2nSJDweD93d3eqc86SkJNW6MZlMcUl778VGx/0OMSeYUE/L4aja+RNMb2+v2quUnx+aLKIlMUmS6Ovro7KykqseuRRzbTfbN+1k5tJipi8OlHHwJ5j8SbkcfuZBfPHqOiSNhCnZyEm/OFbd1mq10t3dzeLFiwPMdyEEXq83YEgZwJgpebTu6Ag4n86oxbA7aOv1evl85w529HTjlWW0Gg1uWebBDes4t2QWbq+Xn6x9HZvbN8VyV7+VC99+g/fPOZ+sMWNoN+hJ0mhIRlJdjNTUVNxuNy6XK2bWTTAp1HV38dzmChweD6dOm86ywshE0IYzqF5R9FPU7To7O1U5BmVdwUmA4WI0YzAHAkat0C5aVTuFMFpaWqivrx9gFQUj2uI5JQM1b9480tLSKCgoYPYhM0JuG9whvvhH85m+eCoOq5OM/HSMJoPase1yuSgpKRlALsrnD755Hq24h3PH/wJ7t0/eQaODf9fep26n1Wpx7R6luztHBQLcXhmHy8WmjnbsbjcSe25ylyzz6ratrN5SjdPrRRYyCwvGcteRR6OVJKxWK2VlZVRUVACo1s1gEgXRYGdPNxe89QY2txsBfNywkz8dcTRHT5o85L4jsYb81e0mTJiA1+ulqqoKq9XK999/H1Gh31DYy1IN+x1GvZI3Umg0Gurq6pAkiSVLlgwZqFNiNkNBGU3icrlYtmxZRE+cUOSVnpNGeo4vwOx2u9m0aRM5OTnk5eUFNL7JsowsywFWSzBebHps0POPExJCyEocGEmSWFBQgEGnw6TVIthT0+drI4C3tm/D6nKSpNOjQcP61hbe31nHiUXFpKWlYTQaWbRoEW63O0CiIC0tbcTp4RerN2N3u9WCP6fXy6ObNkREMLIsxywoq9Vq1Q7pjIyMAYV+GRkZql5xLMSmbDabGndMwIdRKbSL1kVyOp20t7eTk5PjK8+P4EnjP7okHPzrZsINY+8x9/Hla+voMfdSPH8yC48vHVTjRgnmKkHtmpoa300eIbkMBiWobXA4+L8freDOrz7H4rCzqGAsfzziKAwGA6VjxjIpPYP63h51jfnJKdjcbnQa3+eTJAmPLNNi7RtwDr1eT0FBAQUFBQE6MF9tq6HN5aQkv4DFk6cMOUvaHy6vV0nE73ktQusy1pW8/oQQXOjX09ODxWJRC/1CdUcPdrxgJLJIA7HPWTCKxEJ2djb5+fkRX2wajQb37jhEKCjTAidOnMi4cePYuHHjAEKyWx08e/srWLusGJON1G7cSZ+ln2lHTApJXmazma1btwYEc5WamZGSi9frpbKyUhXgkiSJN848d8B2Op2OV08/mz9/9QVVlg6mZmTxu2WH8ofPP2F96y40vro/dBrNkOlrpafmpR21/KtuGxpJwtu4k9N3tXBUeibp6enk5uYO+cQ/tXg679Rtx7n7NzdqdZw1Y2ZEn3u0epE0Gg1ZWVlkZWUBvodasKJfKEtusPUlXKSBiAvBBD/xI4nBCCFobGykpaWFhQsX0traGpXVo9VqcThCTWfwVRNv2bIlYCBcKLencUszfZ195E3wjQBJTjPx3f82hiSY+vp6WltbBwRz/QWthksuTqeT8vJyxo0bR2Fh4ZDbG3Q6bjtiecBrvz/8SK794F3qe3qQEZw1vYRlY8bidrsHtch2Wa08VvY9Lr/vfk1LExcfcihGjxez2Ux9fX2AfEHwseYXjOH+Y47n/75fj8vrZdX0Es4tmRXRZ99bvUhGo3FAd7R/oZ9SXe3xePbGyJL9FqMmODVYrYQSjANYsmQJWq026rhNKMLw14UJJoJQ2/vW6be/7LvYNRqNSpD+PUrBchBCCDIyMti+fTvNzc1qTCYa98JqtVJZWcn06dNDjpmNFDmmZJ46eRWddhsmnZ5Ug0HVtOnp8blTSm2SVqtVP2e7rR+DRhNAMHqthk6Hg5k5uSpBO53OgHjG1q1b1cZFrVbLssLxEWeO/LEvdFOHUvSzWCy0tLTQ2dmpVqUHF/rFolVAkqQs4GUgDXABVwkR1Pi2H2Gvi37b7XbKysooLCxkwoQJ6o2o1WoHdXmCERyDUXRhlCBx8EUWimAmzBxH7rhs2hs7MRh1OPqdHPXjQ9Vju1wuysrKyM3NZfLkySGDuXrJAGY9Pd1W5DEWent7sdlsZGVlqe5FOB++s7OTbdu2MXfu3Jj48hpJIi95z3E0Go1KCqWlpRgMBrxer/o9eDwexqekIod4GEwMktYwGo3qhIJvv/2W/Px8Ojs72bFjh5qtycnJGSA/MRT2xW5q/ziVMj3T5XJRVVWF1+slKysLm80WKwsmG7hXCPE/SZLm4huwduRID7q3MCouUjgorsusWbNUX1jBYC5PKPhnkRQd3rFjxwaQVvD2wQRjNBn4yR/OYN07m+jt7GPK3InMOayEjo4OnE4n69evH1CL408ujn4n7zz2EXarA4NRx44yO4edcRBzD5pLd3c3HR0d6gWq1G4oantNTU20traycOHCuFXe+p9DMfMVslOsmwyNhvuWH8uvP/0Ql9dLklbHA8ccT4p+8DVVWPuo7OumMDOdY8dNoKerK6T8xFC1KPuCBTPU8VJSUsjKylIL/bq6unjiiSfYsGEDV155JStXruTCCy8c1u8ohKgFanf/2UUUg+b3RewVC0YIwc6dO+no6BjguiiIJCvkD4Uwuru72bx5MyUlJYMOIQ9XN5Ocnszycw5R/+7t8XUzt7a2smTJkgDF+OBMUeuOdqzd/RRM8sVwjClGyj7eTMnS4oAO6f7+fsxmM5s3b8bj8SBJElqtlvnz58e0b8Z/nbW1tdhsNhYsWBDyJtdoNOqNeMjESXxx/oVYbDbS9Ho0u+NKGo1GdaX88d+OVj7Z7qu5MWq0vJtfy0PHnhBWfkKxbkI1wMbagolnVgp8Qfa8vDz+9Kc/8fXXX3P33Xfz2Wefxep3vAPfkLX9FqNGMIpVo2RHjEYjixcvHnSkRLRB3r6+Pqqrq1m4cOGQ3dtDFebVlu3kt8fdib3PARJMP6SIoz84Wn0/ZGWuAH+NzHCXtTKbZ/z48ZSXl6PRaNBqtXz33XdqpiYnJycmF6ksy1RVVaHX69Vs1FBQyKZgt1ukWDcKmSq/i1arxe7x8F5nBzqtFqNWixCCyo4OyjraWVgwZoD8hH+lrcvlCrBulN8k1votoyXB6Xa7mTNnDnPnRjbPKxwkSdIC9wCtQoiXRnSwvYy4uUjBUAhg8+bNTJo0acjsSDRBXlmWVdGpww8/PKIb03cxe5E8m8CzFaQchOFwkHzW1M0n/Bl7nwNJ46OMmi/r+M+fX+O8m04LW5k7piiflIwUzM1dGJL02HptHHr60pDnD5UpEkLQ09OD2Wxm586dahl8Xl5e1LEM8MVUysvLycnJYdKkSVHvr8DfuvFPwXu9XvrsdrW6WOD77bWShD1M/MxkMjF+/HjGjx+vKvUr8hNKbCOa2NtoI1Jt4eFCkqQMfNME3hZC/J8kSRohxH6rzTtqFoxysc+bNy+sFq8/IiUYJfCanp5OWlpaxE99jUZDkvQBGttafLaGjPB8jpx8Cy63wNpjQ9KAVqdFCPDKXjZ9VME5N54aNgWdnGZixWXHUvnlFux9DibOGu/r1g6CQrTBmSJJklQRpuLiYhwOh1pr43Q6yc7OJjc3V33aDwaHw0F5eTmTJk2KaXWpP9m4XC5aamuZmJpGY3+/71uUZQx6LSXZ4d1TBcGpbrvdTmVlJTt27KC2tnbQLum9hXAEE8OO8luAUiBFkqSL8U1xPCVWBx9txJ1ghBBqsG/hwoURkQtERjB9fX1UVFRQXFxMVlYWGzdujHhdGo0gTfsmSDkg6UEIJG8N9r4NbKyU0Wg1yF7fg0NxpdJz04asb0nNSmHZyYvCnld5WkeSKUpKSgp42lssFtra2ti6dSspKSlqoDg4mKikumfMmDEgeB4ruFwuNm3axMSJE3l8zmxu/fwTNnd0UJCSwu8PPox0vV61RMLFbvyh9BGZTCaKi4vR6/UBXdLJyckRa8DEE4PJNQy37ikINwM37s9Wiz/iSjBut5vy8nLS0tLIy8uL6ik0FMEo0wnmzZtHampqQLo1EviW4kX9CiQJj8tLXeMW5s49g9OvOYk1967F4/aCAINJzy//ceGILqLGxkba2tqGlSnSarXk5eWRl5eHEAKr1YrZbKaszFcioZCNy+WKaao7FJTSgmnTpqmB9IeOPylgG1mW1TiV8rt4vV6VaMJdC0oMJrhL2l8Dxuv1xlV+YjCEG6ymfLYYHD864aR9HHGLwSjWhdKjo4zfjBThCEaxiHp7ewNEp6KfdWTE5p6DwbAVRDpOZzdOl8S0mSswGNO48I5zKV44hc9Wf0NKejJLzp1NVc1mMjMzyc3Njar9XwihBjUXLlw4YnNfkiTS0tJIS0tjypQpqp5tdXU1fX19FBQUYLPZSEpKinm8QJG4mDVrllp0Fwr+3eCwJ1CskI7y//5FfhA66xNKA0YpfFPkJxTrxp+4YymEFbyeYNhstkQfUgjEhWA6Ojqorq5WrQuIvqM6FGEocZzU1NQBolPDEZ02239MesbnWLu/wu0dT/rYa9Ho9swZOnTVUpadsoj+Pjv/fehd+ixW5hw1A61WS21tLUlJSeTl5ZGbm7vHbJdltM4H0brfBGS82kPYsO000tKymD59elyetnq9HqfTiU6n4/DDD1etm9raWoxGoxooDp5wGS26urrYunUr8+bNi/pmCg4UB1s3Ho8n4iySTqcLULizWq2quJYQQrVukpOT96uJAgci4kIw6enpLF26NCDgOhyC8X8CKV3Lisj3SKHRaHC5NXxbOZ/8/OOZNGlSwM2vXPz9Pf38aukt9HT0ggTvPvkJl/7lPE689Bj6+/vp6OigoqICWZbJzc1lfO7npPEygmQEGnB9SMl4HUm5t494zaEghGDr1q14vV7mz58fciqB2WymqqoKt9tNTk4Oubm+kv9oyK6jo4O6ujrmz58/YqIKZ93Y7XacTqdq3UQau1GsucmTJwfIT/T29uLxeGhraxvxdIKhEIeJAgcE4kIwSUlJA1KNynzq4SBU1/JI4XQ6aW5uZu7cueTl7bFagovnnv/jGrrbezGl+W4ql93Ns7et5sRLj1HrWZQL22w24+3/ELdRIAuBkGX0hiTS9JXEI/Gq1BSlpqaGnTeVnJzMxIkT1TnSFouF5uZmqqurSUtLU2tuBrv5WlpaaG5uDqgAjiU0Gg0Oh4OqqipmzZqF0WgcELuJhGwgsKzf4XCwefNmbDYbTU1NSJKkWjeDSTKEw2AuV8KCCY1RS1MPR3RKGX0yWMXvcNDe3s7OnTtV18H/fMEyC93tvQEVc1q9FrdjIF3o9XrGjh2LzjYZyVWL161kohzYHRo6e1tCZnyGCyU9X1hYyLhx4yLaJ9i16O3txWw209DQgEajCai5UW6+nTt30tXVxcKFC+NW/2G1WqmoqGD27NnqA8S/kjtckd9QZCPLMkajkSlTpqixKkWSob+/P0CSIZLyhqHG6CQIZiBGlWCimSzg9XpxOBzY7fZBK36jgdKiYDabKSkpwWzeM6j+1Qfe4tX738br8bLguLn8+l//D4C5R8xk3Vub8Lq9aLQSLoeLMVNC6wIDNHaewljTFxiNTnzMlITQXYPL4hMtF0KoN3I0Xdb+ULI4xcXF5ObmRr0/+FyLjIwMMjIymDp1Kk6nE7PZzLZt27Db7WRlZeF0OtFoNJSWlg74/v/y5ec8W12p/l2am8fzq86Meh29vb1s3ryZuXPnDnAxlHMGF/kpQWKFbMJZN8HxHEXdbsyYMSrBdnZ20tDQoNbkZGdnh/1dhlKzS7hIAzFqlbzRqNo5HA42bdqEVqtl5syZUd2E4Z4ySne1RqNh0SLflETlCfnOvz/k6Vte8g2ClCQ+f/kbZI+XG566ghWXHUddeQOfvPgVwiXIHZfFH9ZcG/K8NTU1uN0mCsY+j9f7Dgg3Xv1xJGmLmJwOkydPxuVyYTabqauro7+/n6ysLPLy8sjKyoqIRJUb0v9pHwsYjUZV7c3j8VBWVobb7UYIQUVFhZoyNhqNWK3WAHIBKDN38Hz5Js6fNz/icypB4/nz50c0mE8JFOt0OjVQ7G/VBKfBBwsY+xNsUVGRKji1c+dObDYbGRkZAfITyvETerzRYVQtmEhiMIqi3axZs9iyZUtUzWqKGxZs7ipFYQUFBUycOFG9CJWL851/fYjXI4MGX727gHVvb1L3v+r/LuGq/7sEh8MRMsDp8Xh885DS0tRMkVd3ccg1GgwGVeZAlmW6urro6OigpqaG5ORkNSsVypVSivQivSGHA6/XS0VFBTk5OUyePFmtQTGbzWow+9nWppD7PrxpQ8QEo2S5hhs09g8U6/X6kGlwZapAJJkpf8EpRU4zWH7CZDINqmaXcJEGYp+KwTQ2NtLc3MyiRYvUGo7BKieDESq13dfXR3l5OTNmzAhwJyRJ2uPbKy/67eq2uwY8sULdCEpJ/oQJE6LObik6sEqpvJKV8i+eU1yplpYWVe0vXnIObrdblblQ4jr+NShKMPvrr/v5uL1twP6pQ0g6KGhra6O+vp4FCxbE7LMEp8E9Hg/Nzc3k5+erhDNUkZ//sfzlNBWxcEVOs6amZoD8hNVqjYlFKUnShcDPABPwshDivhEfdC9iVJsdwxGMMvLD4/Goinb++yh1Hr9acgtdbd0UTMnjnk9vHRD0DSaY9vZ2tm/fTmlpaYD5KoRAr9djt9vZtGkTUxdOYOemhsBjaTXUVzRRND98k6DSUxSLknz/G1kJSCoWS09PD1qtlhkzZsRFzgH2aOgUFRUFBL6DodfrufqIo3i8ZsuA924eN4mGhgby8vLCWlgKUS5YsCBuaWPFrRszZowqGRFs3UBkLQywRyw8JSWFtrY2cnNzA+QnbDYb7e3tEQfbw0GSpJnAucCx+AJ4H0qS9L4QomJEB96LGDULJlwMxul0UlZWRl5eXoBKHOwhGKfTyRlZl6hKCDvKGjg77xe80ft0wLGU7f2DuYsXLx5Q3alYRUuXLsVut9NY2oZGr0H2+LJH+iSdMoEoLDo6OqitrWXevHnD6nQeCkpAsquri/z8fPLy8lTCSUlJUV2pWNyk/f39VFRUREWU35z7U458+XmcsowWiTWrTmdcShpms5nq6mpcLldAzY1Go6GhoQGz2RxWkyYWUOJHhYWFqkUZSZFfcEVxKCjud7D8xDvvvMO7777Lq6++yvr167nuuusYPz56uVDgDOBZIYQXQJKkV4DDgQTBBCMS4e/e3l71wg6VDVFSlY9f/7y/zAoAXreXtx7/gBU/3zNVUdHOraioQKvVsmjRogGaucEaLsnJyRx/znLW3vcB3e29aHQSHpeHzMJ0HIZ+LBbLgO7lhoYGOjo6WLRoUdyewkrVcnZ2tloEqPTlWK1WOjo62LRpk/q64kpFi56eHqqqqpgzZ06AmNZQSE1NZcPFlw14fcKECerQM4vFwq5du9iyxWftaDQa5s+fH1dy2bhxIxMmTAiYB+6PcEV+odLg/ttD6CCvyWTi9NNPZ926daxYsQKtVjuScorxwLd+f/cRbu7xfoK9FoPZtWsXO3bsYP78+WFvDIWUdtW1h3y/YXNgsFEIQWVlJRMmTGDixIkD3lPU44KfUqZUE3e/fzMPXfkE7fVmJpQUcvkDF+LGpXYvp6amkpeXh8ViQZZlFixYELcydMWqmzhx4oAbxb9yVcl+KOllh8OhZqUikXRQNIDjETRWmjNzc3PZtm0b/f39pKenU1ZWNoAUY9E+oQy/mzhxYlTyFINp3SgPIsWVGiqLlJOTw0EHHTSSj2EB/P3TCcDWkRxwb2PUCEYp/VfSuf39/QPaCULtI8syZ//mFMo+qhzw/ilXnaD+f19fH2azmaKiogByiXQAWk5hNre+ev2A1xWrQZHilGWZ1NRUmpubY9LfEwzFXYl0qoB/ejlY0iE1NVVNLwdbWq2trTQ0NMQ1aCyEYMuWLUiSxPz585EkialTp6rxJUXGMxJB9MHgdrvZuHEjkydPDju7PBKEcqX8ScfpdKpWdTB5xyiL9BZwkyRJLwApwEnA/SM96N7EqLlI4Lvgvv/+e9LT01mwYMGQTy7F6ik9cjbH/uwIPnj6M/W9069dwfipvqd7W1sbtbW1FBQUDAjmjnQAGvgsim3btjF16lTGjh2L3W6no6ODzZs34/V6ycnJIT8/f1jl5/5Q6kLmzJkzrJqKYEmHvr4+Ojo62LhxIxqNRrUoLBYL7e3tLFy4MG5BY0WqMykpialTpwZ8L6FS9f6qdsGC6INBKUGYMmXKoMHpaBHsSinaNLNnzw4ZKFYqg0cCIcSXkiR9AXyFL8j7ByFE/4gOupchDdHSPux+d2X4mAKr1cpXX33FvHnzwvrHwairq8NkMoVN/yqtBBaLhdLSUurr69VB9rEiF6WwbebMmWRmZob8nJ2dnXR0dGC1WqMunFPQ1tbGzp07KS0tjblVBKjqePX19bhcLgoLC8nPz4+LWpwsy1RUVKhzhaKBUnNjNpvxeDzqbKlQui9Op5NNmzZRXFw8qMD7SNHb20tVVRWlpaWYTKYBRX5CCJYtW8aHH344wDWPEKMnaDPKGBUXSVElM5lMEZMLDJ7a9nq9bN68GZ1Op2qsKC5VSEHuYUDJFJWWlobNFOn1erX8PLhwLtJsjxI0jlczIfhcqb6+PrKzs5k2bRpdXV1qADbSpsdI4PV61azghAkTot5faSCdNGmS2hnd2NhIX19fgCC61+tl06ZNTJs2bUQD6oZCX18fVVVVzJs3T41T+Vs3Op2O22+/nenTpycG34dA3F2kuro6LBYLS5YsYcOGDVGpxvunnZ12F0aTAUmS1CfX2LFjA54YyiSCUILc0UAZYxttpii4cE7J9mzcuDHAfVEuVGW4vcvlimvQWJZldc51UVERkiSFdKWUnhyFFKNNvyuFeuPGjYuJpIZ/Z7S/IPqOHTuw2WwUFhbGxdpToNQ5hStFEEJw1113sWvXLt588824ioHvr4ibi6QUbhmNRmbMmIFGo2HdunVRFVjt2rWLpu27+OLZ7+hu7yUtK4WTrzqOTnv7gLlHCimYzWamTJlCWlrasCwXWZapqanB6/Uyc+bMmN30DoeDjo4OOjo6cLvdZGdn09vbS3p6OsXFxXGTfVTqQvLz8yOyKJR1ms1mnE6n6qIMpR+jxEJGGmgdCkqjZ1FRES6XSx2KF032LBIoHd7hxLWEENx3331s3ryZ5557bqSxrAPWRYobwWzduhWdThdQcLRhwwZmz54d8VOnubGFx659liSDibScVDpbO7H2WfntM1eTk7/HLFb8YUWTpaOjg/7+frKzs6O66JQamszMzAFFf7GE3W5XrRpZlsnIyCA/P3/YWZRw8BfmjsY1VeD1etX4Um9vr6qtHDyzSWlO9dfojQdsNhvl5eXMnDkzQK5TyZ6ZzWa6u7sHFUSPBJGQy0MPPcS6det48cUXY+HWHrAEEzcXqbi4eEBhXbSaMLZuG7ZeB3nT8+jt6UXWyqSYUnH2udTyI/9grk6nC2hYUyYybt26lfT0dPXmCHUTKxbXpEmThnUzRgq73U55ebk6glaZfNjR0aGq5w/W8BjNeYKFuaOFVqsdoB/T0dHBzp070ev15ObmkpqaSk1NDSUlJSGD4LFCf38/5eXlIbvIhxJEV6ywSLJ8SplAONF0IQSPPfYYX375JatXr46rSt6BgFEdHRutql1KZgoCQVtrG8YkA1npmfSY+0jJ8P3wgwVzFQElpY6lp6dHlX1MSkoiPz9fvYmHyhTFCqEEs/0lLoMbHv1jJdHEQyIV5o4G/vIGxcXF2O12mpub2bRpE0lJSaq2TrRSnJFAIZdIqo3DCaLv2LGD/v5+MjIyyMvLCynarpwnlDYN+K63J598kvfee4/XXnstbvVDBxLi5iL5B1sVVFdXU1BQEHHU32w28+ZT71Dz4U5MySaELDjhkqNZdvKisNMVI0F/fz/t7e10dHTg9XrVkZ/xzEYoVbPR9C45nU41bhNpPESppYnn2BIIFIpKSkqis7MTs9msxpWUm3ikdTaKuxLupo8GirVoNpuxWCyqILryEFIspHAk9swzz7BmzRrefPPNWFc+H7AuUtwIRomJ+KOmpkYNxg2Fnp4eysvLMRgMTB1XjKW1m8y8DPIn5aq9IyNJQQshaGhooL29XW0BcLvd5ObmxqRozh+Kpm1paemwn3rB8ZD09HTy8/MDnsSKhRavWhoFCokpdSH+8LcWLRYLer1etcKiXZNiiQ1nikEkUATR29ra6O3tVbuvQxH4iy++yLPPPsvatWvjsZYEwUSLUARTV1dHcnLykDEOZajazJkz2bFjBwsXLvQtJkbFc7Iss3XrVmRZDsgUKUVz7e3twwoSB0MpBOzt7WXu3LkxC+AqN3F7ezsWi4WkpCR0Oh02my2uMgiwRygqUhJTqp7NZrM61SBc4Zw/FAspXuTiv76ysjJmzJiB2+0OCGhnZGSQlJTE559/zuOPP87atWtHXK0bBgmCiRahCKa+vh6tVhu2lV0IQW1tLT09PcybNw8hBGVlZSxZsiRm5BJppkgJEnd0dNDd3U1aWhr5+flhg8Sh9lf6cJQ0fbxQU1NDR0eHah0pFkOsb0xFKGr+/PnDssQ8Ho9qhSmFc6EC7z09PVRXV4e0kGIJhVxmzZoVEDhWAtplZWVcffXVdHZ2cvXVV3P++edTVFQUj6UcsAQzqkHeoSpzKyoqSEpKUoeq+U8AjEVlrpLBmTx58pBVl8FB4t7eXtrb29UgsXITh7rRvF4v5eXlcU93+xfqHXzwwb5ZT7sbCZXuan8rbCTr8FfUG25cRafTBRTOKfGQuro6NR5iNBpVNy+e5KJkDWfOnDkgK6UEtJVGzNdee42vv/5arb9JIHLEzYIRQgyYItDa2orNZhvwIyl1FMqgd/9jfPXVVyxduhQYfmUu7NE9iUWmSMn0dHR0DMj0KONExo0bR2Fh4YjOMxiUZkKDwcC0adPCquArVlhPT0/UVpgCRSiqtLQ0btWqNpuNhoYGWlpa1FR9Xl7esAsmB4NyvQ2WWv/www+54447ePvtt4c9uSEKHLAWzKgSjOJuTJs2TX2tp6eHyspKZs6cOSCL4/V6+eabb9QbI5p50P5ob29nx44dAf0ksYKS6Wlvb8fpdOJyuSgqKmL8+PFxs1wUCykrK4vJkydHtI+/FdbZ2YnRaFRv4nACSYoyoBJDiqebZ7FYVG0ajUYT0EA6WGo5WjidTjZu3DgouXz22WfccsstvPXWW6PVX5QgmGgRimAUrZKZM2cCvlYApYPYP3XrH28B1BvDYrGQnJys1rAMZaoLIaivr8disTB37ty4Bj+7u7upqqqisLCQvr4+tbM6Pz8/ZuXrsEdYaaQWks1mU60wWZYD4jZKH9n27dtxuVwxbZkIhc7OTnVaQjDZKQr/SlYqEmIMB4VcBpMG/fLLL7nxxhtZu3ZtXC3QICQIZjhwOp0Bf/f09NDY2Mjs2bPZvn07vb29lJaWBhCFfxu8orXh/57VaqW9vR2z2YzBYFD1aoNjIUqQFaCkpCSuN4hiIflnVpTO6vb2djVIrFToDvcpHKkwd7RQitHa29vVuIPdbicpKYmSkpK4WWLgs2oVZcNIAseKnINCjEpWaihXyuVysXHjxkG7r9etW8c111zDf//732F1go8ACYIZDoIJxmq1sn37dsCn1B48TznaYK6i5q7EQhSy0el0VFRUBOjZxguNjY20t7czb968sBaSf5m92WzGaDSGJcZwUErY412S7/F42LRpEx6PByEEqampatwm1uJUygjf4abWg3vPMjMzyc3NHeBKRUIu33//PVdccQVvvPFGxG5nDJEgmOHA5XIFqNp1d3ezfv16SkpKBqSqR5opcjqdtLe309raSl9fH7m5uRQVFUVf/SnLaDz/Q5J3IjQTkXUnQQjrR3Eh7HY7c+bMicpCGixIHApKgDoW1ayDQcnkKdkvRcpBiduMpGguGG1tbTQ0NDB//vyYuK7+PV1dXV1qpi8jI4OqqiqmTp0atiervLycyy67jDVr1lBcXDzitQwDCYIZDvwJpru7m8rKSiRJ4tBDDw3YLhaVubDnRpw+fToul4v29nbsdrsqaTlUcReAzvZ7tJ5P1Q8u647Ak3xnwDbKGFqj0Rg2gxMpgtsBlEpixeRXWgzinbaNRChKKZpTWiyGKxXa2tpKU1MT8+fPj4tkpxACm81Ga2sr9fX1JCUlMWbMmJANj1VVVVx88cW8/PLLlJSUxHwtESJBMMOBQjBKMHfevHmUl5dz8MEH+w4eo+I52CM5GZwpUkrs29vb6evrGzzw6t2Jsf9nCFJB0oKQkejDmfwE6KYCPrO8vLycvLy84cojhoVSiNbe3o7VasVoNOJwOFi4cGFcS/+VwPH48eMjFooKrnqOVCp0165dtLS0DIi9xRqKEHhRURHp6elqVkpZq9lsJi0tjcsuu4wXXniB2bNnx20tEeCAJZi4F9pt27aNvr4+lixZgk6nUy2aWJGLf6YolOSkv+SAEnhVVPcH9POIHkDykQuApEEICYkeBHuCrFOmTImLqJJ/IVp9fT27du0iMzOTjRs3htViGSmGKxQ1HKnQ5uZmWltb4zobCfaQy5QpU9QaFn8Zj66uLh544AHeeustFixYwPr165k1a1ZcY3U/VMSVYCorK9HpdAMmCMSqMtc/U6TUTwyGYElLpZ+ntrZ2d3FXKhNTUpFEH5AM2EBKRWimq1298ZZ0UGRGlbEuyrgXJRayc+dODAbDsFO1/oiVUFQoqdD29vYAqVCPx0N3d/eokItCmKEybRqNBqvVyqZNm/jf//5HcnIyn3/+eYJc4oS4ukhWq3WARaFU5o6UXNxud8wyRf43ha13EyXjnsVk6AFtAR7TbVh686mpqYm7BIIQgurqajQazYAMmz/8s2fg6z3Kz8+PSjNGUYeLd1bK4XCwdetWNfCqDFyLJB4WLZTJjpMmTQprjTU1NXH22Wfz6KOPjnRIWixxwLJbXAnG4/EE9B7JsszGjRsRQjBmzJhhz1aOpqdoOPC/gV0uF7IsM2/evJiJN4WCMuojNTVVFeaOBIourVJJHMkNrFhjodThYo36+nq6urqYN28esiwHNDtmZmaqFbojrVNS0usTJkwIe03s2rWLM888kwcffJDDDjtsROeLFHa7HavVOlTdUoJghgOFYILjLYrgk9lsVuMOkdaEKJmiWKq1hYIS2+ns7CQ3N5fOzk48Ho+a5YnVyFOIXph7sOP438ChAq/K9xdvGQSAHTt20NfXFzKN759WViq0hysV6vV62bhxI+PHjw8rBdLa2spZZ53FPffcw1FHHTXszxSMrq4uzj77bPr6+jAYDDz44IOUlpayatUquru70Wq1nH322Vx22cA53n5IEMxw4PF48Hg8gwZz/a0FjUajFqCFypqEyxTFGkIItm7dOmCygFLYNZz0dzgoc6gnTZoUU2tMuYHb29vp6uoiNTWV5ORk2tvb4zKLOhhKHGn27NlDWif+UqFmszkqqVBlPlJhYWHYDFhHRwdnnHEGf/7znzn++OOH/ZlCoba2lm3btnHCCSdQUVHBlVdeyaeffspRRx3Fxx9/HOlhEgQzHHg8HtXFiCTe4nA4aG9vp729HSGEmv1JSkpi586ddHV1xb2nyOv1UllZOaSrElX6OwwUPZJI51APF8pIlx07dmAwGALkJkYSJA53rtraWhwOB7Nnzx4W8UYqFaqQy9ixY8P2DXV2dnLGGWdw6623smLFimF/rkjQ1NTEueeey7PPPstBBx3ErFmzyM3N5aGHHhpKZC1BMMPBBRdcQEZGBitXruSggw6KKnugXGRtbW309fVhMpmYPXt2XCtZFamFwsJCxo0bF/F+wX1HQ00wgD1ykKMRBwkWivJvdBRCxEygSqludrvdzJw5MyYuZLBUqNJZnZGRQWVlJWPGjAlLLt3d3ZxxxhnceOONrFq1asRrGQoXXXQRJ5xwAueccw4ejwedTsdjjz3Gxo0beeSRRwbbNUEww4HD4eC9995j9erVbNy4kUMPPZRVq1ZxyCGHRFTLoRS1ZWZmkpSURHt7Oy6XKy66uUpWpbi4eET6H/6atJ2dnZhMJrX7W7G8RkuYG3y1J7t27QpbNasEiTs6OnA4HMN2+4QQ1NTUIMty3BokFZGq9vZ2mpubSU5OZsKECSHjd729vZx55plcffXVnHXWWTFfiz+8Xi/XX389SUlJ3HXXXQHvlZWVce+99/LMM88MdogEwYwULpeLDz/8kDVr1vDNN9+wbNkyVq1axeGHHx7S5VEyRcFFbR6PRxVqjlUcRAl8xtqaUGIL/gFtk8lEd3c3CxYsiGt1LviEojo7O5k3b15E1mOw25eZmakOhBvM7VNiVsCg6fVYQJZltaUhKytLJUdAdfnS0tI466yzuOyyyzjvvPPithbwXTvnnXceJ510EldccYWqBKDX6/F6vVxyySWcd955Q8V+EgQTS7jdbj777DNeeeUVvvjiCxYtWsTKlSs56qijMBqNamvBUJki5YZoa2vDarWSnZ1NQUFBVLN5/AfcxzvwuWPHDpqbmzEYDGg0GrV+JdbnVcTGrVZr1I2YCoKzPKmpqWqWJ1heo7q6Gp1ON+K+rEjWVF5eTk5OzoBsm2KJ3XrrrXz88cfMnz+fm266iUMOOSSuhX033HADL7zwAsXFxfT19VFYWMjxxx/Piy++SFpaGpdcckkkFlSCYOIFr9fLF198werVq/nkk08YO3YsHR0d/Pe//40q8KnUWLS3t9Pb20tmZiYFBQWDBl2bmppobW2ltLQ0roFjJeXd3d2tThfwV8Jzu90q2Yw0/a3o9Lrd7piVvwfr8Oj1etXtq62txWAwxHW+Nuwhl+zs7LA9YHa7nR//+MesXLmSSZMmsXbtWu67776oChCjhcvlQqfTjbSOJ0Ewo4G7776bN998k0WLFvHpp58yffp0TjvtNI4//vioYhWhgq6KpolSel9bW0t/fz9z5syJ6xNOiU14PJ6wynCxSn9HWgk8UtjtdrVtQZIkJkyYoJJjPKAUIWZlZYUlF6fTyfnnn8+KFSv45S9/ub+V/u9Xi40G+xTBvPfeexx99NHodDpkWeb777/nlVde4d1332Xy5MmsXLmSE088Mao4iX9gUDH1nU4nKSkpcVdrU4S5jUZjxE/44cZBFAkJk8nE1KlT4/65Nm/eTEpKCuPHj1fJcSRB4sHOVVlZSUZGBpMmTQq5jcvl4mc/+xnLly/nmmuu2d/IBRIEs3ehPMFWr17N22+/zZgxY1i5ciUrVqwIq60aCkqXrUajwePxYDKZKCgoiEjfN1oMR5g7GP5dyl1dXWGnAgQLRcUTym+RkZEx4FzBKeWRtgIo5JKenh72c7ndbi6++GKWLl3Kb37zm5iRS6gK3WnTpnHFFVewY8cOvF4vDz30EKWlpbE4XYJg9hUobsDq1atZu3YtWVlZrFy5kpNPPnnQ9LJSMTtx4kTGjBkTEFfo6OjAaDSqLQsjjcfESpjbH8FTAZT0d2ZmJlVVVRQUFIQdaBcrKHGQrKyssNaE/7b+QeKUlBSVHCP5foUQasHjlClTQm7j8Xi47LLLmDlzJr///e9jarmEqtA9+OCDmTNnDj/5yU+orq7moosu4ptvvonF6RIEsy9CKexavXq1OpD81FNP5dRTT6WgoEC94KxWK5WVlYOqySvp5I6ODnQ6nVpFHG1fTLyEuf2hpL937dpFY2MjJpNJrQeJdWWuAsUiy83Njbpfyp/MOzs70el0g0pvCiHYvHkzycnJYQedeb1errjiCiZOnMgdd9wRV7dIqdDt7u6mrKxMtR4XL17MZ599FosgcoJg9nUoM3zWrFnD66+/jlar5eSTTyYnJ4e6ujp+9atfRVwFrAQx29vbVTFxpWVhMIyWMDcECkWlpaWp5OjfYhGr9Lcip5mfnx8TK8lfelOW5YAGUiEEVVVVJCUlMXXq1JD7y7LMNddcQ1ZWFn/5y1/iOjEC9lTo3nTTTdTW1qqvH3XUUbz88suxeJAkCGZ/ghCC5uZmfv/737N27VpmzpzJiSeeqKYvo3naORwONZ2szA8qKCgYcPOOljC3sqZwQlHB6e+RVj1H0u8zEiijbjs6OrDb7QghSEtLY9asWSGJQ5ZlbrjhBgwGA/fff39cySW4Qnfq1KnU1NSoFkxpaSllZWWxONUBSzCjOpt6tCBJEkajkZ6eHlX5/9VXX+VXv/oVfX19rFixgpUrV0aU2UlKSmLChAlMmDBBFRKvrq4OkG5wOp3qVMJ4F+sNJRRlNBrVEbxK+nvHjh3YbDays7PJz8+PuBBR0VgZN25cxFq90cJgMKid0Js3b1b1g7799lsyMjJUSVONRoMsy9x8880AcSeXUBW6Rx99NKtXr+acc87h/fffV7WlEwiPA9KCGQxms5nXX3+dNWvW0NHRwUknncSpp54adXOe2+2mo6ODxsZG+vv71YBuLPujgqEIRc2ZM4e0tLSo9lXmVPsXIg6W/lYC1RMmTBiqE3jEEEKwZcsWtFqtWg3sX17Q2dnJo48+CvgI9Omnn45r7RKErtB98sknufjii1WNoCeffDJWXfAHrAXzgyMYf3R1dfHmm2+yZs0ampqaOO644zjttNMiLq9XhsLPmjVLvRn6+/vJycmhoKAgprKQsRSKCtaKCU5/K+QymPRkrKD0MUmSxPTp00N+X16vl5tuuolvv/0WSZIYN24ca9asiWtgN0YVupEiQTAHOnp7e1m7di1r1qyhtraWY445hlWrVrFgwYIBF5lSCWyz2QaQUSidGKVlYbg3hMVioaamJi79UsHpb6PRSH9/P1OnTo2bW+R/7pqaGoQQYSuPhRDce++9VFdX8+yzz6LT6bBYLHHVz9kLSBDMDwlWq5V33nmH1atXU1VVxVFHHcWqVatYsmQJQgi+//570tPThyzHl2VZdUt6enrIyMigoKBgyKpcf3R0dFBXVxdyMHys4XQ6+f7770lLS8Nms6np5Pz8/LgIU23btg1ZlgcllwcffJDvvvuOF198Ma79Ygo8Hg/t7e2jOfgeEgTzw4XD4eDdd99l9erVbNiwAZ1OxzHHHMNtt90W1QUf7JYE90eFgjJetbS0NOp6nGjhdDrZtGkTxcXFamZKSScrCoPDmV4QCkr9ksfjCduuIYTg0Ucf5ZNPPmH16tUx//xbtmzh0ksvZcaMGfz73//m1Vdf5ZZbbqGgoICcnBxeeeWV0Ww5SBDMDx19fX2sWrWKmTNnYrPZWLduHQcffDArV64Mq2kTDv4zmTo7O9UB87m5uWrwUhlSFu8JiLAn7T2YdKeSQVMmLQw3/a24ly6XK2xgXQjBE088wdtvv81rr70WF92cvr4+nn76acrKynj88ce5/vrrOeecc1iyZEnMzxUBEgTzQ4fL5eLLL79UFendbjeffvopq1ev5osvvmDx4sWsXLmS5cuXR+VO+A9VM5vNmEwmNBoNLpcr7kPKYA+5DFblHAxF9Ms/qB1p+lvR6x1MSuKZZ55hzZo1anV2vPDUU0+xadMm/v73v3PkkUcCPkvzxhtv5OSTT47beUMgQTAJhIfH4wnQtJk3bx6rVq3imGOOieoGkWWZmpoaLBYLWq0Wg8GgTlmIh4ukiI6PpPI4mvR3bW0tdrt9UDHwF198kWeffZa1a9fGXU70qaeeYvPmzfztb39TNXR37drFwQcfzPbt2+NuOfrhgCWYuHyD4WbFmM1m/vCHP2A0Grn//vsB33iLK6+8EqvVSk5ODk888URUHdL7AnQ6HcuXL2f58uV4vV6++eYbVq9ezZ133smMGTNYtWrVkJo2SlzC6/Vy8MEHI0kSNpuNtrY2Nm3aFDBjOxYBV6Vgb+bMmSOaL6WMhs3LywuIM9XU1ASkvxsaGtSsWzhyWbNmDU8//fSokIv/+gGVTMaOHUt+fj5er3c0CeaARVwsmFCdqO+++y5XXnkl9957b8AFvXz5cv71r39RXFzMY489Rk1NDffcc89wTrvPQZZlNmzYwCuvvMJ7773HlClTOPXUUwdo2igd4lqtNmwtiNIfpejPKpbNcFyI/v5+ysvL4zrRwD/9vWvXLgCmTp1Kfn5+yHjVm2++yUMPPcTatWvj3sel4KmnnqK8vJz77rsPt9uNXq/n3Xff5fnnnx9KpDvWOGAtmLi7SEon6plnnsl3331HT08PGo2GBx54AL1ez0UXXcT7778P+NLDxxxzDN9+++1IT7vPQZE6UDRtCgsLOfXUUzn22GN56qmnOPPMMyMWinI6nWozptfrVfujIsnujKQaeDjYuXMnvb29TJkyRe05UqwxpZv6f//7H3/729946623RrW+ZfXq1bzzzjvceeedHH/88YwbN46SkhL+9Kc/jZoFtRsJghkulE7Uhx9+mJtuuokf/ehHfPTRRzz88MNcf/31PPjggzz//PO+kwlBaWkp5eXlIz3tPg2lY/iFF17g8ccfZ9asWZx11llDatqEgv9saiW7U1BQEFLbVyGX0WjIBAJ0iP3jMUr6e/Xq1fznP/+hv7+fV155haVLl8Z9TfsoDliCiVsdtNfr5dprr2XMmDGcc845mM1mjjvuOAAOPvhgdu7cSXZ2tmrygy81O5LZzPsLJEli9uzZtLS0cMcdd/Doo4/S3d3NOeecw8knn8zjjz9Oa2srQ5A/4GsWHDduHAsWLGDhwoUkJydTW1vLt99+y/bt2+nt7VUzVRUVFcybN29UyKWhoSEkuQCYTCYmTpzI4sWLSUtL45prruGPf/wjTz/9dNzXlcDoIi4WTKhO1NNOO43rrruOI488kjfeeIP33nuPBx98kAULFvDGG28wefJkbr75ZubOncu55547vE+zn8FmswW4Ncq4kTVr1vDGG2+g1Wo55ZRTWLVqFWPHjo2q3sTr9arzo/r6+nC73ZSUlAQIccULjY2N6jymcEWEX375JTfeeCNr164d7arZfREHrAUTF4IJ1Yn6l7/8hcsuu0zNOjz66KNkZ2ezYcMGrr76ajV7cu+99+6Pos0xhxCCpqYm1qxZw2uvvYbH4+GUU05h5cqVTJw4MeLvSGmSnDhxIt3d3fT29qpztLOysmL+XTc1NdHR0UFpaWlYcvn222+57rrrePPNN38QFmsEOGAv+LgQTKw7UUOlvceMGcNFF12E3W7HarXy8MMPs2TJkgMi7R0MIQStra28+uqrvPrqq1it1og0bbq7u9myZUtAk6QiJN7W1qb2R/lrrowECrkMNkny+++/54orrlCt1lgjuAXgqaee4umnn8Zut3P22Wdz3XXXxfycMUCCYPYmQqW9ly5dyty5c7ngggv45JNPeOSRR3jppZcO6LS3go6ODlXTprOzU1Xr8+/rUeZfz58/P2ypvaK50tbWNujUgkjQ3NxMW1sbpaWlYfctKyvjsssu49VXX6W4uDi6Dx0h/FsArrvuOq699lreeusthBAcc8wxPPTQQ8ydOzfq4z7xxBOcffbZ8YpfHbAEs19UEk2dOlXVZ83KysLr9TJ27Fj6+/sB38V96KGH0tTUhF6vVy/e8847j2OOOWavrTteyMvL4+c//zk///nPsVgsvPnmm9x22200Nzdz/PHHU1BQQENDg1rUGA6SJJGVlUVWVpZat9LW1kZtba06BSCSkS4tLS20trYO2tqwefNmLrvsMl555ZW4kQtAWloaqamppKSksGbNGn7605+qazrrrLP4/PPPh0Uwd955J3PnzuWyyy7jww8/3O+t4tHCqKjpxBK///3vueqqq7jiiiv473//y9///neeeeYZLrzwQpqamgIEklJSUrDb7XtxtfFHdnY2F154If/973/5+OOPEUJw11138cUXX/CnP/2JDRs2IMvykMeRJImMjAymT5/OsmXLmDJlClarlfXr17Np0yZaWlpwu90D9tu1axe7du0alFy2bNnCJZdcwgsvvMCMGTNG/JkjgV6vH3A9pKWlqQ+lSNDa2sqKFSv48MMPAbjuuutYunRpQJ9Sd3c3p59+Og6Hg4cffph169bF7kMcANgvLBjYI8CspL1vuOEGfvvb33LEEUcwY8YMrr/+eq6//vofZNpbQVJSElu3blVV+d9++20efPBBtmzZwlFHHcXKlStZsmTJkO6PJEmkpaWRlpZGcXGxOnLk+++/V+dS5+fnY7FYaG5uZsGCBWGPuW3bNi688EKeffZZZs+eHY+PHRJarXZAGURjY2NUBFdQUMDll19OYWEhZrOZ++67j1WrVgVsc/fdd3PLLbdw7rnn8uc//5lPPvkEjUbD4sWLY/VR9mvsFxZMT08Pp556KsXFxdx1113IshwwPkIRRyouLqatrY2dO3cC8Mgjj/DTn/50L6169GE0Gnn99dfJzc0lNTWVs88+m5dffplvv/2Wo446iieffJKDDz6YX//613z++ed4PJ6IjpuamkpRUREHHXQQJSUleDwevvvuO6qrq8nNzQ1p2YCviveCCy7gySefjNUExIjhcrlYsWIFzz//vDqX6e233+akk06K+BiSJHHcccfxu9/9jqKiIu69914uvPBC9f1169bx+OOP88ADD+B0OnnwwQd5/fXXOeyww7j88stpbm6Owyfbv7BfWDB33nknZWVl9Pf388QTT1BYWMjtt9/O5ZdfjslkIi0tjX//+99oNBqeeOIJfvKTn6hp73POOWfA8ZSUr8PhQAjBU089RX5+fsixoB0dHVx++eXq9Md//etfYQew76swmUysWrWKVatW4XQ6+eCDD3jxxRe57rrrOOSQQ1i1ahWHHXZYRJo2ycnJmEwmDAYDpaWlWCwWKisrB8xjamxs5LzzzuOxxx5j0aJFo/Ap9yA1NZWenh4OPfRQDjvsMA455BCEEPzxj3+MqgWgs7OTm266icrKSv7xj3+g1+v57W9/q77f2trKWWedxbx58zCZTNx9991ceumlLFq0iOOOO47W1lbGjRsXj4+4/0AIMdi/fQJOp1N4vd6YHrOjo0MIIcTTTz8trrnmGnHjjTeKZ599VgghRFVVlTjooIOEEEKce+654rPPPhNCCPHuu++KM844I6br2JtwuVzivffeE7/4xS/ErFmzxM9+9jPx2muvia6uLtHf3x/y344dO8Qnn3wiuru7A163WCxi69at4qWXXhIlJSWiuLhYPP/883v7Iw4bb7/9tliyZImor68XM2fOFD/96U/Fu+++K4444oiA7W6//XYhhBBr1qwRzz33nLj77rtFWVlZtKcb6j7cb//tFy6SwWCIubp7bm4uZrOZDz/8kCOOOIK1a9fy4x//GICZM2fi8XhUSYPDDz8cgOOPP56qqqqYrmNvQq/Xc9xxx/Hoo49SVlbGhRdeyPvvv89hhx3Gz3/+c9566y0cDoe6fUdHBzt37mT+/PkDrB1lHpNS/n/GGWfw4osvctVVV432xxoxzGYzZrOZN998U7VWPR4PycnJA9o33G43bW1t3HjjjZjNZrZv3868efP2xrL3SewXLlI88Nprr3HjjTeyePFili9fjt1uDwhUpqWl0dnZOUA6YJTGWIw6gjVtvv76a1avXs0f//hHZs6cyZQpU2hpaeGhhx4K60p1dHRw1llncdddd3H88ccDRNRPNVzES6A7Nzc3IHY3adIk6urq+OabbwYEs0855RRaW1v54osvaGpqYsWKFTFdy36PIUycAx4vvPCC+MlPfiKKioqEx+NRX583b55wOBxixowZ6mtOp1MsWrRobyxzr8Hr9YoHHnhAjBs3TixatEicdtpp4sknnxStra0BLlJDQ4NYsmSJeOutt+K+pjVr1oiZM2eK5cuXizPOOEPIshz3cwohhMfjEW1tbfE49F53ZeL17wdpwciyrFoiHo+HnJyckGNBjUYjEyZM4JtvvmHZsmU8/fTTnHnmmXt59aMLSZLYvHkzZWVlZGVlUVZWxurVq/nHP/5BYWEhK1eu5LDDDuOiiy7i5ptvjipLM1x89dVXPP3006Mu0K3o2CQQOfaLVoFY49NPP+XKK69k3LhxFBcX89e//hWbzRZyLGhdXR0///nPsdvtFBcX8/jjj8d9PtH+ACEEmzdvZvXq1fzzn//k1ltv5fLLLx+Vc+9lge544IBtFfhBEkysESrtXVVVxe9+9zvS09OZMmUK//73v9Hr9WzYsIHf/OY3uFwuiouL+ec//7nfE5YQYlQ74PeyQHc8cMASzIEZsRxl6HQ6nn32WT7++GMuvvhiHnjgASwWC5999hmff/45eXl5PPfcc7jdbi6//HJeeuklPv/8c8aOHcvjjz++t5c/Yoy2vEYoge4E9k0kCCZGCE57/+QnP1HFzZUGzW+//ZbS0lJVFvO8887js88+25vL3i+hVA6/++67lJSU7PcW4IGMBMHECK+99hqHHHIIbreb5cuXq6+3tbWxZs0azjzzzBE33yXga65cuHAhJ5xwAu+88w6PPPLI3l5SAoNgv3Zc9yWcdtppnHbaabz44ov86le/4tlnn6W+vp7zzz+fRx99lMzMzJDNd/EQXTqQMXbsWCoqKvb2MhKIEAkLJgbwl0NQ0t6ffvop5513Hv/85z9ZunQpsixzyCGH8NVXX9HV1QXAP//5zx9UM2YCPzwkCCYGUESMTjjhBL755hv+/Oc/c+aZZ+J0Orn88sspKSnhueeeIzU1lXvuuYcTTzyRgw46iHnz5rFs2bK9vfwEEogbEmnqOMHhcGA0GoedYQmV+lbcqYqKCq6++mo++ugjgAMy9f0DwwGbpk4QzD4Ms9lMbm4uzzzzDBs3buT++++nvr6e66+/nrKyMmpqanC73Rx66KG8/fbb5ObmctNNN1FYWMiVV165t5efQOQ4YAkm4SLtwwhOfXd1dXHVVVfxyCOPqK0OB0rq+6mnnuKoo45i2bJl3HfffXt7OQnECD9YgjGbzVx77bWDbvPrX/96lFYTGv6p7yOOOIJf/vKX3HvvvQHjZQ+E1Hd1dTUvvvgiH3zwAV988QVvvPFGIlN0gOAHRTBtbW1cccUVrFu3jttvv53bb7897LZOp5PW1ta9Oif7tNNOo6amhlNPPZVrrrmG7777jquuuooTTjiBxsZGTj755AMi9e2v/q/T6VT1/wT2f/yg6mCEEJSUlHDttdfS3NyMTqfj/vvvH7DdmjVraGxs5NFHH+X8889XGx9HE6E6vrdv3w74hscvWLCAtWvXYrVaue666+jq6iIrK4t//vOf+138pampiYMOOkj9Oy0tjfb29r24ogRihaGCvAcUJEn6FbAUuAa4GWgTQtwdtI0OWAOcI4RwSJJ0MPAocLYQYssorvVI4CGgGdgO/EYIYdv93iTgGSHEkbv/PgG4DV+wcI0Q4q+jtc5YQJKkPwOVQoj/7P77FmCrEOKVvbuyBEaKHxrBJAP/A2z4MmQ/BY4C3hVC9Eq+nPK/8N2o84GG3e9XAAuAG4UQD+2FpccEu8nzv0ASvs94oRBipyRJucAfAacQ4trd2xbhI7hUoBO4WAjRFad1HQrcBJwMpADvAccJIfavYFICA/BDI5gLgQLgA+BG4C5gGnC5EOKo3ZZBDrAFeAU4G/haCDFPkqQMIFUIsV/PopAkKVcIYZYk6QJ8pPk7fETyayFEj992nwCXCiG2S5L0C2C6EOL6OK7rd8Cp+IjvD0KI9+J1rgRGDz8YgpEk6SjgEuAvQDHwOfAL4GGgUAhRtXs7CfgH8Bk+K+cgIcQNe2XRccJui+Ve4HVgErAEyABk4GrADTwphDhu9/apwIdCiINCHjCBBMLgh5RF2gTcCVQCVwGf4IvDXKWQy24cii8Go8FHRvutSxQKkiSdBnwF6PF9B6fhi+ecDPwd+BswHvCPsvYDplFdaAIHBH4wFkwwdlsqJiVw+kODJEnnAiuAhcBcIYQsSZIJn2V3HvCQEOL43duOBx4VQiQk8xOICj8kCyYAu9Xcf1DkIkmS/++twxe83Q4cvvu144Fvd79WIEnS5N2vXw48O0rLTOAAwg+qDiYBDpckKSD1DUwGHpUkyQt0AJfttmYuBp6TJEkLfA28tJfWnMB+jB+si5RAAgnEHz9YFymBBBKIPxIEk0ACCcQNCYJJIIEE4oYEwSSQQAJxQ4JgEkgggbghQTAJJJBA3JAgmAQSSCBuSBBMAgkkEDf8f3VU9nUlNuktAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70        4\n",
      "121       4\n",
      "135       4\n",
      "831       4\n",
      "1624      4\n",
      "         ..\n",
      "62906    18\n",
      "63095    18\n",
      "63096    18\n",
      "63577    18\n",
      "63578    18\n",
      "Name: 省份, Length: 519, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "# 使用KMeans聚类\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(x)\n",
    "predict_y = kmeans.predict(x)\n",
    "print(predict_y)\n",
    "# 可视化3D\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "ax = plt.subplot(projection='3d')\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']  #中文显示问题\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['STXINWEI']  # 指定默认字体\n",
    "mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题\n",
    "ax.scatter(x['总分'],x['省份'],x['学校类型'],c=predict_y)\n",
    "ax.set_zlabel('学校类型')  # 坐标轴\n",
    "ax.set_ylabel('省份')\n",
    "ax.set_xlabel('总分')\n",
    "plt.show()\n",
    "print(x['省份'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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